Shooting

Shooting Your First Video.. 27 Shooting Your First Still Images.. 28 Shooting with Digital Zoom.. 29 Using the Macro Function.. 29 Using the Flash.. 30 Using the Self-Timer.. 31 Continuous Shooting.. 31 Recording a Voice Memo.. 32

Getting Started

Attaching the Strap.. 17 Loading the Battery.. 17 Charging the Battery... 19 Inserting the SD Card... 19 Using the LCD Screen.. 20 Turning the DV Camera On/Off.. 21 Setting the Date and Time.. 21 Selecting the Language.. 22 Formatting the SD Card.. 23
Customizing Shoot Settings
Selecting Video Format.. 33 Selecting Image Size... 34 Selecting Image Quality.. 34 Selecting a Drive Mode.. 35 Adjusting EV Compensation.. 35 Adjusting White Balance. 36 Adjusting the Sharpness.. 36 Turning On/Off Voice Memo. 37 Selecting Image Color Mode. 37

Before Shooting

Using Modes.. 24 Using the Menus.. 25

Recording Audio.. 38

Playback
Playback Mode.. 39 Playback Zoom.. 41 Slide Show.. 42 Rotating Images.. 42 Specifying the Startup Image.. 43 MP3 Playback.. 44
Power Saving.. 49 Language... 49 USB Switch... 49 File No Reset... 49
Downloading to Your Computer
Getting the DV Camera Ready.. 51 Connecting the DV Camera.. 51 Copying Files... 52 Browsing Files.. 53 Value-bundled Software.. 53

Deleting and Printing

Deleting Files... 45 Deleting Current.. 45 Deleting Selected.. 45 Deleting All.. 46 Printing Images.. 46 Printing Selected.. 47 Printing All... 47

Appendixes

Appendix AViewing on a Television Set. 54 Appendix B Web Cam. 55 Appendix C Using the SD Card.. 56 Appendix D Maintenance.. 57 Appendix E Troubleshooting.. 58 Appendix F Warning and Error Messages. 66 Appendix G Menus and Factory Defaults. 67 Appendix H Specifications.. 68

DV Camera Setup

Brightness.. 48 Beep.. 48 Date... 48 Time.. 48 Date Stamp... 48 Video Out... 48 Format.. 49 Reset.. 49 Copy to SD.. 49 Frequency.. 49

Safety Precautions

Before you use the digital video camera (DV camera), be sure to read the following safety precautions, which detail the proper operation of the DV camera and its accessories to prevent injuries or damage to users or equipment. This symbol indicates situations where improper use of the DV camera may result in harm to the DV camera operator. This symbol indicates situations where improper use of the DV camera may result in damage to the DV camera. Children should not be allowed to have access to the DV camera. They could injure themselves with inappropriate use of the DV camera or by becoming entangled in the carrying strap. If a child swallows a camera battery, get the child medical attention. Subjecting the DV camera to water or any other liquid, or allowing condensation to form, may start a fire or give the user an electrical shock. If liquid gets inside the DV camera, turn the DV camera power off by removing the battery or unplugging the power cord (with dry hands). Allowing the DV camera to slowly adjust to temperature changes (as in transferring the unit from outdoors to indoors) will help to prevent condensation. Let any condensation evaporate before using the DV camera. It is not advisable to leave the DV camera in a car on a hot day for any length of time. Exposing the DV camera to too much heat may warp the DV camera casing or damage the batteries. Battery damage may result in fire, burns or electrical shock. Another source of possible burns is heat from the flash or DV camera itself. Touching the flash after it has been used extensively can cause burns. The DV camera body itself may burn your hands if the DV camera has been operated for a lengthy period of time.

Do not drop batteries or let them be banged around. This could damage the casings, causing the batteries to leak. If the internal parts or components of the battery come into contact with your eyes or your mouth, flush the affected site with water and get medical help. Do not short-circuit the battery terminals, and be sure to cover the terminals before you throw batteries away. If the terminals contact metal, they may overheat and explode, causing a fire.
Do not leave the DV camera in humid or dusty areas. Dust and humidity can cause the DV camera to short circuit, leading to a fire. If the DV camera gets anywhere near a strong magnetic field, it may not work correctly, or the pictures taken may be ruined or adversely affected. Use a soft, absorbent cloth to clean the surface of your DV camera.
Black or bright (red, green, blue, and white) dots may sometimes appear on the LCD screen. These are merely misfiring pixels, and have no effect on the recorded image. If you aim the DV camera at the sun, or shoot the flash close to someones eyes, you may injure your eyesight or that of someone else. If you see smoke or smell a burning odor coming from your DV camera, turn the DV camera off. If you continue to operate the DV camera, it could cause a fire or give you an electrical shock. Do not clean the DV camera with flammable liquid, as these may cause a fire.

Introduction

The Microtek Take-it MV320 is a digital video camera (DV camera) that applies the ASF (MPEG 4) recording video format to capture motion pictures at a resolution of 640 x 480 or 320 x 240 pixels, offering superior image quality and super audio. Equipped with a large 2.0" color LCD screen, the DV camera allows you to focus clearly on the images you want and to review live images as shots are being taken. Key features: 1/2 3.21-megapixel CMOS sensor 7 megapixels interpolated resolution 2.0" LTPS LCD screen with 270 rotation 4X Digital Zoom/4X Playback Zoom 16MB internal flash memory Secure Digital (SD) media external memory support Plug-and-play USB 1.1 and Video connection Macro function 5 flash modes with selectable Red-eye Reduction 10 sec. self-timer delay Functions as Digital still camera/Digital audio recorder Functions as Web Cam/Mass storage device Picture/video/audio player

2 Insert and push the SD card into the compartment until you hear a click. Charger LED
Ensure that the orientation of the SD card matches the SD card marking in the SD compartment.
3. Close the SD card compartment cover securely.
*SD card is optional and is not included with your purchase. 19
To minimize the risk of fire and/or explosion, do not use the AC adapter to charge alkaline batteries.
To remove the SD card, make sure the DV camera is turned off, open the SD card compartment cover, push the card in, and release; the card pops out.
NOTE: Incorrect insertion of the SD card may cause a memory error, blocking you from storing any data in the SD card. You may lose or damage data if you remove the SD card while it is in operation.

Using the LCD Screen

The LCD screen is used to frame the subject while you take your shots. In addition, you will be able to view the stored video/audio clips and still images on the LCD screen as you record it. When the DV camera is powered on, the LCD screen is automatically activated, displaying the DV settings relevant to your current DV mode. To use the LCD screen: Swing to open LCD screen from the side of the DV camera. The LCD screen is designed to allow adjustment for viewing angles. You can adjust the LCD screen to tilt back to 180 degrees and to tilt forward to 90 degrees. To protect the LCD screen from dust and dirt, close it when not in use.
For more information, see the Appendix Using the SD card.
Turning the DV Camera On/Off
Press the POWER button to turn the DV camera on. When the DV camera is turned on, the green Mode indicators start blinking and will stay on steady in the Video mode, and the LCD screen is activated as well. Press the POWER button again to turn off both the DV camera and the LCD screen.
POWER button Video indicator
Setting the Date and Time
Set the date and time on your DV camera before you use the DV camera for the first time. The DV camera can also add the time and date to your picture. To set the date: 1. Press the MENU button. 2. Press to select the Setup ( ) menu option at the top.
When selected, the settings are shown on the LCD screen and display the current state of the DV camera.

DV Camera Auto Power Off

In order to prolong battery life, the DV camera or the LCD screen may be set to turn off automatically after a period of inactivity. If auto power saving is not required, you can disable the auto power saving function in the Setup menu mode (refer to page 49).
3. Use the / button to select Date, then press the button.
A Date adjustment screen appears on the screen, allowing you to set the date.
4. Use the / button to toggle through the YYYY, MM, and DD formats.
The selected option is highlighted with color.
5. Use the / button to enter a new value for the selected option. 6. Press the button to confirm your changes and to return to the Setup menu screen. 7. Press the MENU button to exit the menu.

The time should also be set accurately on your DV camera. To set the time: 1. In the Setup ( ) menu screen, use the / button to select Time, then press the button.
A Time adjustment screen appears on the screen, allowing you to set the time.

Selecting the Language

The DV camera allows you to select the on-screen display language. To change the language setting: 1. Press the MENU button. 2. Press the button to select the Setup ( ) menu option at the top. 3. Use the / button to select Language. 4. Press the button to display the available language options. 5. Use the / button to select the language you require. 6. Press the button to confirm your choice. 7. Press the MENU button to exit the menu.
2. Use the / button to toggle through the HH, MM, and SS formats.
3. Use the / button to enter a new value for the selected option. 4. Press the button to confirm your changes and to return to the Setup menu screen. 5. Press the MENU button to exit the menu.
NOTE: The date and time setting may be shown incorrectly if the DV camera has not been in use for a long time. Please check the date and time setting and adjust accordingly.

Formatting the SD Card*

Before you insert an SD card into your DV camera for the first time, you will need to format the SD card with the DV camera. In most cases, the newly purchased SD card can be directly used for storing images. To prevent possible malfunction, it is best to format the SD card before you shoot. In addition, you can use the format function to erase all previous files stored in the DV cameras internal memory or in the SD card. To format the SD card: 1. Press the MENU button. 2. Press to select the Setup ( ) menu option at the top. 3. Use the / button to select Format, then press the button.
A Format screen displays on the LCD screen, allowing you to format the installed SD card or to cancel.
4. Use the / button to select OK, then press the button.
The SD card will begin formatting. When formatting is completed, the LCD screen returns to the Setup menu screen.
5. Press the MENU button to exit the menu.
NOTE: The format function removes all files from the DV cameras internal memory or SD card permanently. Before you perform the format operation, ensure that you have saved all desired images and video/audio clips from the DV camera. A MEMORY FULL message appears on the LCD screen if the internal memory or the SD card is full. To resolve -- Connect the DV camera to your computer, then transfer the stored files from the DV camera's internal memory or the SD card to the computer. - Remove all the files in the SD card or the DV cameras internal memory, or use a new SD card. Individual images can also be deleted to free up memory. Improper use of the SD card may damage its stored files. For better storage, transfer the files from your SD card to your computer, and back up the files to a hard disk or CD-ROM.

Video o o o o o o Setup o o o o o o o o o o o o o o

Photo o o o o o o o o

Playback o o o o o

Page 46 43

2. Press the MENU button to exit the menu.
Page 49 49 (o: Available, : Unavailable)
Using the Multifunction Button
The Multifunction button provides Up, Down, Left, and Right arrow selections for navigating the menu screens. In addition, the button can perform different functions, depending on the selected DV mode, as shown by the table below.

Using the Shutter Button

Press the Shutter button all the way down to take a shot or to record.
NOTE: By default, a beep sounds when the Shutter button is pressed to indicate that a recording has been made. If the beep feature is turned off, however (in the Setup menu), a busy dialog box is the only indicator that something has been recorded.

Button

Playback Photo Zoom In/ Play/ Pause/Pan Zoom In Next/ Fast Forward/Pan Flash Zoom Out/ Stop/Pan Zoom Out Previous/ Rewind/Pan

Movie Zoom In Zoom Out

Holding the DV Camera
Hold the DV camera with your right hand through the strap. Adjust the strap so that you can operate all the buttons while holding the DV camera steady.
NOTE: Avoid obstructing the DV camera or flash when shooting photos or audio recordings. Hold the DV camera securely with your hand to prevent shaking and dropping it. Use a tripod for best results, especially when using the self-timer or in poorly lit places when the flash is turned off. Do not touch the lens or the flash.
The OK button, located in the center of the Multifunction button, allows you to confirm your choice and switch between nine-image display and singleimage display in Playback mode.
Shooting Your First Video
Video mode allows you to record digital video clips in ASF (MPEG 4) and AVI format with resolutions of 640x480 or 320x240 pixels. The video clips can be stored in the DV cameras internal memory or in the SD expansion card, the length of which is determined only by the size of your available memory. To record video clips: 1. Press the POWER button to turn on the DV camera.
The DV camera should start up in Video mode. Check for the Video mode icon ( ) in the top left corner of the screen. NOTE: If the DV camera is not in Video mode, press the MODE button to show the mode screen, then select Video mode ( ). You can change the various settings that influence the quality and appearance of the video clips you record. The following table lists the available functions that can be manually adjusted in the Video menu. The flash is inactive in Video mode.

Function Description Video Format Size Quality EV Compensation White Balance Color Mode Page 36 37
2. Compose your shot using the LCD screen. 3. Press the Shutter button and release to start recording.
Video recording begins, with the elapsed recording time shown on the LCD screen. For best results, hold the DV camera steady to pan when recording video clips.
4. Press the Shutter button a second time to stop recording.
The video is recorded and is automatically saved in the memory. The DV camera is then ready to record another video. You will be able to view your video in Playback mode. 27
Shooting Your First Still Image
In addition to making vide clips, the Take-it MV320 can be used to take digital still images in JPEG format at resolutions of up to megapixels (interpolated). 1. Press the POWER button to turn on the DV camera.
The DV camera starts up in Video mode when you turn the DV camera on. You will need to switch to Photo mode to take still images
NOTE: The effective shooting distance from the lens to your subject is 3.28 ft. (1m) to infinity in normal mode, and 0.5 to 0.8 ft. (15 to 24 cm) in Macro mode. The use of a tripod is recommended to prevent blurring when taking still images. You can change the various settings that will influence the quality and appearance of the still images you take. The following table lists the available functions that can be manually adjusted in the Photo menu.
Function Description Size Quality Drive Mode EV Compensation White Balance Sharpness Voice Memo Color Mode Page 37 37

2. Press the MODE button

The LCD screen shows the mode screen with the four mode options.
3. Press the button to enter Photo mode ( ).
When the DV camera changes to Photo mode, there will be an icon appears in the top left corner of the LCD screen.
4. Compose your shot using the LCD screen. 5. Press the Shutter button to take a shot.
When you press the Shutter button, you will hear a sound to indicate that the still image has been taken (unless the sound is disabled), and the captured image remains on the screen for two seconds. The DV camera is ready to take another still image when the Busy message vanishes from the LCD screen.
Shooting with Digital Zoom
The DV camera is equipped with a digital zoom function. You can zoom in on distant images by pressing the ( ) button on the rear of the DV camera without changing the shooting distance; the maximum zoom scale is 4X. Use the ( ) button to zoom back out. 1. In shooting mode, compose your shot using the LCD screen. 2. Press the / ( / ) button until the desired zoom level is reached.

A bar appears indicating the zoom scale of the DV camera.

Using the Macro Function

You may want to shoot images or video of subjects from a short distance. Use the Macro function in this case. The effective Macro shooting distance is 0.5 to 0.8 ft. (15 to 25 cm) from the lens to the subject. To take a Macro shooting: 1. In shooting mode, move the Macro focus lever located on the front of the DV camera to Macro mode ( ).
When the Macro focus lever is in the Macro position, an icon ( ) appears on the LCD screen.
3. Release the / ( / ) button when you have reached the desired zoom level. 4. Press the Shutter button and release to shoot.
2. Compose your shot using the LCD screen. 3. Press the Shutter button to take a shot.

Before

NOTE: The zoom level is disabled when you change modes.
To bring the DV camera back to normal shooting mode, move the Macro focus lever back to Normal position ( ).

Using the Flash

The flash serves as a built-in supplemental light source for taking pictures in environments with insufficient lighting. The effective flash range is 1.5m to 2m. The flash with red-eye reduction allows you to reduce the phenomenon of red eyes, which occurs when a subject is photographed directly while using a flash. By pressing the Flash ( ) button you can toggle through the following flash modes: Auto (Auto flash) The flash fires when ambient lighting conditions require its use. Auto with red-eye reduction The flash will fire when ambient lighting conditions require its use and will reduce the red-eye effect. Forced flash The flash fires every time you take a picture, regardless of ambient lighting conditions. Forced flash with red-eye reduction The flash fires every time you take a picture regardless of lighting conditions and will reduce the red-eye effect. Flash off The flash will not fire.
To take pictures using a desired flash mode: 1. In Photo mode ( ), frame the subject using the LCD screen. 2. Press the Flash ( ) button until the desired flash mode appears on the LCD screen.
The factory default flash is Flash off.
3. Press the Shutter button to shoot.
Auto with red-eye reduction
NOTE: The flash is designed to fire twice in order to detect correct shooting distance of the subject. When you press the Shutter button with the flash enabled, you will see the flash fire twice and hear a sound to indicate that a still image has been taken (unless the sound is disabled). The DV camera is then ready to take another still image.

Option (Auto) (Daylight) (Cloudy) (Tungsten) (Fluorescent) Description Adjusts white balance automatically. Use outdoors for sunny days. Use outdoors for cloudy days. Use indoors to correct tungsten/light bulb illumination. Use indoors to correct fluorescent bulb illumination.

Adjusting the Sharpness

The Sharpness function allows you to sharpen (highlight) or soften (blend) the edges of pixels within the image. Higher sharpness makes these edges visible, and a lower sharpness makes these edges softer. This function is available only in Photo mode.
Option (Soft) (Normal) (Sharp) Description Softens the edges of pixels in the image; good for portrait shots. No special effects; no adjustment. Sharpens the edges of pixels in the image, increasing image clarity.
To access the Sharpness setting: 1. In Photo mode, press the MENU button. 2. Use the 4-way arrow button to Sharpness, then select the Sharpness option you require. 3. Press the button, then the MENU button to exit the menu.
The selected Sharpness icon appears on the LCD screen. You are now ready to shoot.
To access the White Balance setting: 1. In either Video mode or Photo mode, press the MENU button. 2. Use the 4-way arrow button to select White Balance, then select the White Balance option you require. 3. Press the button, then the MENU button to exit the menu.
The selected White Balance icon appears on the LCD screen. You are now ready to shoot. 36

Normal

Turning Voice Memo On/Off
The Voice Memo function allows you to add a maximum 10-second audio clip immediately to the captured image you just made. The Voice Memo file will have the same basic file name as the captured image with a different file extension .WAV. This function is available only in Photo mode. To access the Voice Memo setting: 1. In Photo mode, press the MENU button. 2. Use the 4-way arrow button to select Voice Memo, then select either On or Off. 3. Press the button, then the MENU button to exit the menu.
When Voice Memo is turned on, the Voice Memo icon appears on the LCD screen. You are now ready to shoot.

A confirmation screen appears, allowing you to erase all or to cancel.

Printing Images

The DV camera supports the DPOF (Digital Print Order Format) function that enables you to print still images stored in memory on a DPOF printer or for professional print services. You can print either an individual image or all your images. This function works only on captured still images. To access the image print function: 1. In Playback mode, press the MENU button. 2. Use the / button to select Print. 3. Press the button.
A Print options menu appears, allowing you to print all images, to print the selected images, or to cancel.
All files are removed from memory and will no longer be available for viewing.

Printing Selected

1. In the Print options menu, choose Select, then press the OK button.

Printing All

1. In the Print options menu, choose All, then press the OK button.
A confirmation screen appears.
2. Use the / button to select the image you wish to print.
The selected file is enclosed in a yellow border and comes with a confirmation screen of the number of copies to be printed.
2. Use the / button to set the number of copies, then press the OK button.
All the stored images appear in a nine-image display, and each image is imprinted with a DPOF mark.
3. Use the / button to set the number of copies if desired. 4. Use the / button to select more files to print and to set the number of copies if required. 5. Press the MENU button twice to exit the playback menu.
3. Press the MENU button twice to exit the playback menu.
The DV camera provides some manual settings that enable you to customize the functions of the DV camera for your specific needs. Use the Setup menu to set the various setting for your DV camera. The Setup menu can be accessed from any mode except Audio mode, and is always the same. To access the Setup menu: 1. Press the MODE button to select , , or mode. 2. Press the MENU button. 3. Use the arrow button to enter the Setup menu ( ). 4. Use the / button to navigate through the available functions for the DV camera. Read this section carefully before making any changes. If you make a mistake and do not know how to correct it, you may have to reset all settings to the factory default.

If the USB Switch is set to the incorrect mode, you will need to do the following: a. Press the MENU button to enter the Setup mode ( ).

Connecting the DV Camera

1. Make sure your computer is turned on. 2. Connect the DV camera to your computer, using the provided USB cable.
Connect the narrow end of the USB cable to the DV cameras USB connector.
b. Select USB Switch, then select USB Mode. c. Press the button, then the MENU button to exit the menu.
Connect the flat end of the USB cable to the USB connector of your computer.
In a few moments, your computer will automatically detect the DV camera.
NOTE: With an SD card installed in your DV camera, the system reads both contents from the SD card and the DV cameras internal memory. Without an SD card installed in your DV camera, the system reads contents from the internal memory alone. The SD card may be placed into any card reader device. The files stored on the card can then be read from the card reader and copied to the computer. * SD card is optional and is not included with your purchase. 51

Copying Files

After connection, the DV camera appears as two removable disks on the desktop, indicating the DV camera is connected to your computer successfully. Files can now be transferred into any folder on your hard drive. 1. For PC Users: Double-click the My Computer icon, then double-click Removable Disk.
2. Double-click the DCIM icon, then double-click the 100_MTDV folder to display the files. 3. Drag and drop the files into any folder on your computer.
File Structure File names vary, depending upon the type of recording being described. The 100_MTDV folder containing the files are named IMAGxxxx.JPG for still images, IMAGxxxx.ASF for video clips, and IMAGxxxx.WAV for audio clips or voice memos.

Windows 98SE/2000/Me

Windows XP
For Mac Users: Double-click the newly created icon (e.g., Take-it or Take-it.SD) on the desktop.

Mac OS X

Mac OS 9.X
Mac OS X Mac OS 9.X With an SD card installed
Without an SD card installed
A folder (DCIM) appears, containing all the stored still images or video/audio clips that were made with the DV camera.

Browsing Files

Double-click a file in the folder that contains the files downloaded from the DV camera. The file opens with your systems default video, audio or image application.
NOTE: If you cannot play the transferred video files (e.g.*.ASF) with your systems default application, follow the procedures below. For Windows users: Install the bundled Ulead VideoStudio application contained in the Microtek CD-ROM. For Macintosh users: Visit the http://www.videolan.org/ vlc website to download the video player application.

Value-bundled Software

The software bundled with the Take-it MV320 provides user-friendly tools for image editing and management, allowing you to open, view, print, email, upload to web or manage transferred images. For more details, refer to the CD-ROM accompanying your DV camera.
Appendix A Viewing on a Television Set B. Viewing the Image on a TV Set
In addition to being viewed on the DV cameras LCD screen, recorded video clips or still images can be viewed on a TV screen. The TV screen can be used to frame the subject during video recording. 1. Make sure that both TV set and DV camera are turned off. 2. Connect the DV camera to your TV set, using the provided Video cable.
Connect one end of the Video cable to the DV cameras TV-output connector.
To view images on a TV set, select the video output system first.
Connect the other end of the cable to the Video-In jack of the TV set.
A. Selecting a Video Output System
1. Press the MENU button to enter the Setup mode ( ). 2. Use the 4-way arrow button to select Video Out, then select NTSC or PAL.
NTSC: For USA, Canada, Mexico, Taiwan, Korea, Japan, etc. PAL: For Europe, Australia, China, Singapore, etc.
3. Turn on the TV set, and select the channel through the Video Input. 4. Power on the DV camera, then press the MODE button.
To play back, press the mode ( ). button to enter Playback
3. Press the button, then the MENU button to exit the menu.
To use the TV screen to frame the subject during video shooting, press the button to enter Video mode ( ). The image will appear on the TV monitor.
Appendix B Web Cam (Windows Only)
A Web Cam is a digital camera attached to a computer that sends images or live video streams to a Web page and is ideal for video conferencing. To use the DV camera as a Web Cam, you will need a computer, Web conference software (not provided by Microtek), and an Internet connection. To set up the DV camera for use as a Web Cam, follow the steps outlined below. 1. Install the Camera Driver.

SD Card Handling Precautions
The following precautions are provided for you to safely get the best performance from your SD card.
*SD card is optional and is not included with your purchase. 56
Abuse of the SD card may cause it fail in an operating situation. Do not attempt to disassemble, bend, shake, or apply force to the card. Do not subject the SD card to water, condensation, dust, sand, or high humidity and temperature, as well as to static electricity and noise. The SD card is equipped with a write-protected tab to prevent inadvertent recording over saved images. You cannot record when it is set to LOCK. You may lose or damage data if you remove the SD card while it is in operation. Store the card in its supplied case. Do not touch the card terminals or the card with any foreign material.

Appendix D Maintenance

Always follow the Safety Precautions that came with the DV camera. Use a soft cloth, tissue or lint-free cloth to clean the DV camera body. Use a lens brush to remove particulate matter from the lens, and then clean the lens with a soft eyeglass lens tissue or cloth. Do not use synthetic cleaning solutions or other solvents to clean the DV camera body or lens. If you are unable to remove marks or dirt from the lens, contact Microtek Customer Service. Do not rub the LCD screen forcefully or apply excessive pressure to it to avoid scratching the surface. Do not use water, detergents, paint thinner or benzene to clean any part of the DV camera, as this may damage the DV camera body or LCD screen. Use a lens brush, soft cloth, or eyeglass tissue/cloth to clean the LCD screen.
Appendix E Troubleshooting
DV Camera Problems The DV camera does not power on. A1: The DV camera is not powered up. Press and hold the POWER button for a few moments to see if the READY LED is lit. A2: The battery is inserted incorrectly. Insert the battery with the correct polarity as marked in the battery compartment. A3: The battery has poor electrical contact with the terminals in the battery compartment. Clean the battery terminals with a dry cloth, reinsert them, and try to power on the DV camera again. A4: Battery power is depleted. Recharge the battery if it is rechargeable, or replace the batteries with new ones. A5: The incorrect type of battery is being used. Replace with four new AAA-sized alkaline batteries. Replace with a new Lithium ion battery (Nokia 8210 compatible). A6: The battery/SD card door is open. Close the battery/SD door securely. A1: Your DV camera has a two-minute time-out setting for saving battery consumption. You can disable the Power Saving feature in the Setup menu. Press any button to turn the LCD screen back on or the POWER button to turn the DV camera on. A2: Battery power is depleted. Recharge the battery if it is rechargeable, or replace the battery with new ones.

For Windows 98SE, check to see if Camera Driver has been installed on your computer. Go to the Windows Control Panel and double-click Add/Remove Programs. If the DV camera driver does not appear in the list, you must install the DV camera driver from the Microtek CD-ROM. Check if the DV camera is detected by your system. 1) Right-click the My Computer icon and click Properties. The System Properties window appears. 2) Click the Hardware tab and then click the Device Manager button. Expand the Disk drives tree; the removable disk drive should be in the list. Mac OS cannot find the newly created drive icon (Take-it, Take-it.SD) on the desktop. A1: The USB Switch setting is set to the incorrect mode. Enter the Setup mode, and set USB Switch to USB Mode. A2: The DV camera is disconnected to the computer. Make sure one end of the USB cable is connected to your computer and the other end to your DV cameras USB connector. Make sure the USB cable is properly connected to both the DV camera and the computer. A1: The USB Switch setting is set to the incorrect mode. Enter the Setup mode, set USB Switch to Web Cam. A2: The DV camera is disconnected from the computer. For Windows Users: Make sure one end of the USB cable is connected to your computer and the other end to your DV cameras USB connector, then turn on the DV camera. Check to see if Camera Driver has been installed on your computer.
The DV camera does not operate in Web Cam mode.
Go to the Windows Control Panel and double-click Add/Remove Programs. If the DV camera driver (for Web Cam) does not appear in the list, you must install the DV camera driver from the Microtek CDROM. Check if the DV camera is detected by your system. 1) Right-click the My Computer icon and click Properties. The System Properties window appears. 2) Click the Hardware tab and then click the Device Manager button. Expand the Image Device tree; the DV camera should be in the list. For Macintosh Users: Make sure one end of the USB cable is connected to your computer and the other end to your DV cameras USB connector, then turn on the DV camera. Check to see if Camera Driver has been installed on your computer. For Mac OS 9.x, 1) Go to the Apple menu and select Apple System Profiler. The Apple System Profiler window appears. 2) Click the Devices and Volumes tab; the DV camera related drivers should be in the USB tree.
Appendix F Warning and Error Messages
The table below lists warnings and error messages associated with operating the DV camera. Follow the recommended solutions to resolve errors.
Warning and Error Messages Battery low! Card error! Problem Battery power is depleted; DV camera will shut down soon. Incorrectly formatted SD card The SD card contact area is smudged or soiled The SD card is damaged DV camera is faulty The memory space is full recorded The SD card is write-protected Solution Turn the DV camera off; replace or recharge batteries Reformat or replace the SD card Use cotton swab dipped with industrial alcohol to clean the contacts Replace the SD card Contact Microtek Customer Service Erase some data or use an SD card that has ample free space Set the write-protected tab on the SD card to the unlocked position

Automatic Statistical Processing of Multibeam Echosounder Data

Brian Calder

Center for Coastal and Ocean Mapping & Joint Hydrographic Center University of New Hampshire, Durham NH 03824

Abstract

This paper presents the CUBE (Combined Uncertainty and Bathymetry Estimator) algorithm. Our aim is to take advantage of statistical redundancy in dense Multibeam Echosounder data to identify outliers while tracking the uncertainty associated with the estimates that we make of the true depth in the survey area. We recognize that a completely automatic system is improbable, but propose that significant benefits can still be had if we can automatically process good quality data, and highlight areas that probably need further attention. We outline CUBE and its associated support structures, and apply it to a dataset from Woods Hole, MA. We illustrate CUBEs output surfaces, show that the algorithm faithfully maintains significant bathymetric detail, and how the algorithms auxiliary outputs can be used in the decisionmaking process. Comparison with a selected sounding set shows that CUBEs outputs agree very well with traditional approaches.

Introduction

The Data Processing Challenge Processing of Multibeam Echosounder (MBES) data is a challenging task from both hydrographic and technological perspectives. There has been an emphasis in the past on improving methodologies and technologies for the collection of data without a corresponding emphasis on new methods for data processing. We are now faced with the situation that we can collect data much faster than we can conveniently process it. With modern shallow water systems running at up to 9600 soundings/second, data collection at the rate of approximately 250 million soundings/day/system is possible. Processing at that rate using conventional methods is more difficult: it is no longer realistic to continue with the traditional hand-examination processing methodology. We have to find some acceptable solution to handle automatically as much of the data as possible. Ironically, collecting dense MBES data may be the best solution to the problem of MBES data. Multibeam systems and operating procedures have advanced to the stage where most data is mostly correct most of the time. With suitably dense MBES data, we should be able to construct statistically robust estimates of depth in almost all cases, and use the consistency of the data to indicate areas where there are difficulties that required further attention. An automatic method also provides an objective approach to the problem. Human operators are currently making subjective decisions about every single sounding that they select as not for use, with the time burden and quality assurance/control concerns that this subjectivity implies. Regardless of training, experience and dedication, this will eventually lead to mistakes that may be untraceable. An objective automatic method should mean that the operators only have to examine the data that does not correspond to the norm. That is, we should have the operators examine only the data that really needs work, not routinely examine every sounding being gathered. In this way, we reduce the number of subjective decisions that have to be made, reduce operator fatigue and burnout, and facilitate faster processing of data. The traditional hydrographic approach has been to consider the quality of the component soundings that are represented on the smooth sheet (i.e., the primary archive of the survey). Previous work on automatic processing has maintained this idea, whether attempting to simulate the human operator [Du et al., 1995], nominate dubious soundings by a robust measure of local neighbor properties [Debese, 2001; Debese & Michaux, 2002; Eeg, 1995], or looking at statistical consistency in an area [Ware et al., 1992; Gourley & DesRoches, 2001] (see [Calder & Mayer, 2002] for a more extensive discussion). However, what this

approach answers is the question How good is this measurement? and not the question How well do we know the depth at this point on the seafloor? We contend that this latter question is the one that we should be answering; that is, the processing goal is to determine the depth in the survey area, rather than select soundings. Once we have determined the depth sufficiently well across the survey area to build a suitable surface model, we may make hydrographic decisions on what is significant and what is not. The restatement of the hydrographic question above is intuitively appealing. It is inherently statistical in nature, accepting that our knowledge of the depth may be limited, and subject to update as we gather more data. It implies that we can and should use more than one sounding (if available) to update our information on depth, using redundancy to deal with the noise inherent in each measurement. And it focuses directly on the quantity that we want to measure, aiming to get as close as possible to the correct answer directly, before subsequently applying any safety constraints mandated by good hydrographic practice (see, e.g., [Smith et al., 2002]). However, it also poses some problems. How do we estimate the errors in the measurements? How do we distinguish normal statistical variations from outliers? How do we utilize information from a set of neighboring soundings to estimate the true depth? The extent to which we can resolve these problems defines the advantages we can expect from an automatic processing method. Hydrographic Concerns As conscientious Hydrographers concerned with safety and charting, the notions of estimated depths, surface models and combinations of measurements should raise some concern, if not eyebrows. It is important to point out, therefore, that we do all of these things already. For example, we estimate depth by measuring travel time of sound and converting it, more or less well, into range, and thence through some ray approximation of acoustic refraction into depth and distance. We make an implicit prediction of surface continuity in every chart constructed through the use of selected soundings or contours. We combine measurements from a myriad of systems to make every MBES measurement. Each one of these measurements is in error, and so therefore is any combination of them. Hence, it makes no sense to talk of any one sounding as being the depth all of the soundings have some error, and this error is not uniform across the swath, between systems, or across all survey environments. Consequently, unless we take account of these errors, we may be deceived about the depth in an area due to noise in the MBES system, in the motion sensors, or in the GPS. Currently, we deal with data by experience and practice. We expect certain MBES to fail in certain ways; we ask operators to make subjective decisions on what is real and what is not; we strip out data past a certain off-nadir angle, even it appears to be normal, based on the intuitive feeling that outer beams are more noisy. However, none of these solutions is really adequate as data volumes increase with a modern MBES survey, can we really affirm that we have inspected every sounding? We suggest that a statistically justified estimated of depth is not only a reasonable method of proceeding, it is a required method {Smith et al., 2002]. It is certainly a more objective solution to the problem. The CUBE algorithm We propose an algorithm that takes uncleaned MBES data and attempts to estimate the true depth at a collection of point locations arranged in a grid over the survey area. At each point, or node, we maintain an estimate of the true depth and the posterior variance of this depth, which we update as more data becomes available in the area. In order to deal with noise or outlier data, we implement a monitoring scheme that checks new data against current estimates; if the data is inconsistent (outwith limits based on the expected error associated with the data), then it is modeled and tracked separately. Hence, each node is represented by a collection of potential depth estimates, or hypotheses, each with an estimate of depth and its posterior variance. After all data is assimilated (or on demand), we attempt to choose the most likely hypothesis at each node according to a suitable metric our goal is to determine the true depth by choosing the hypothesis that appears most likely given, e.g., number of depth soundings which agree on the depth, closeness to neighboring depths, or consistency of data. We thereby construct a set of point estimates over the survey area, each theoretically representing the best statistically supportable estimate of depth in its location. These point estimates may then be connected into a surface description of the area, which is more readily manipulated and processed. Since the heart of the algorithm is concerned with the estimation of uncertainty in the measurements, we call the algorithm CUBE (Combined Uncertainty and Bathymetry Estimator).

The rest of the paper outlines the CUBE algorithm (for a more detailed mathematical development, see [Calder & Mayer, 2002]), and describes the trial implementation that has been built to test the ideas presented. We then describe a hydrographic survey in Woods Hole, MA, which illustrates the behavior of CUBE, and the use of diagnostic indicators to guide operator effort.

Method

Estimation at a Point The basic element of CUBE is an estimation node, defined at a point location with respect to some fixed projected coordinate system. We can define the location of a node absolutely, and the node therefore represents a true point in space. An immediate consequence is that the node only has to consider a single depth, since there can only be one seafloor at a point location. Therefore, the node does not need to track horizontal uncertainty (its location is known exactly), but only vertical uncertainty in the true depth at the location. Another immediate consequence of this basic definition is that the estimator we build only has to determine an unknown constant, which makes the estimation task significantly simpler. A final consequence is that, under the null hypothesis that all of the depth soundings in the area are unbiased (i.e., on average, report the true depth), then it does not matter in what order we process the data. That is, we can take it all at once or one point at a time, and in any order. We can in particular sequence the data by the order in which it is recorded. Each node thus receives a sequence of data points representing the soundings in its immediate vicinity. The estimator then has to determine the best estimate of true depth from this sequence, and we may treat the problem from the perspective of time-series estimation. Error Models, Information Propagation and Optimal Estimation
CUBEs estimator starts with a quantitative estimate of the errors associated with each sounding. For each
data point, we determine the predicted horizontal and vertical error using the model of Hare et al. [1995], which utilizes a propagation of variance argument to convert errors in the MBES itself and those of its auxiliary sensors (GPS, IMU) into a predicted error for each sounding. The model is detailed, requiring many properties of the systems in use to be known (e.g., sample rate, accuracy of attitude measurement and patch test, etc.) The configuration is also specific to the survey platform in use since it depends also on the offsets between the various instruments; once configured, however, the computation is straightforward. A typical error model response is shown in figure 1, although this will of course change with MBES in use among many other factors, and should only be taken as illustrative. The error model provides the basic error measurements required, and is the heart of the rest of the system, but it only provides information about the errors at the nominal location of the sounding, and contains both horizontal and vertical components. Since we are using a set of fixed nodes that have no horizontal errors, we must propagate the information implicit in the soundings to each estimation node location, and combine the vertical and horizontal errors. Our propagation of information method is based on a local bathymetric model that assumes that the local surface consists of at worst a constant slope; as long as we only use soundings that are sufficiently close to the estimation node, this is a reasonable assumption (figure 2). To ensure that we do not use soundings inappropriately, we also increase the uncertainty associated with a propagated sounding as a function of distance through which the sounding has been propagated. This is implemented by scaling the vertical uncertainty associated with the sounding by a factor that increases quadratically with distance (figure 3(a)). To incorporate the horizontal uncertainty, we assume that the sounding could be up to a fixed fraction of the horizontal uncertainty associated with the sounding further away from the estimation node than the nominal location (figure 3(b)). Augmenting the distance by this fraction factors in horizontal uncertainty in a reasonable manner: the higher the uncertainty, the larger the distance scale factor, and hence the higher the reported uncertainty at the node. Indeed, the scaling process provides many desirable features: soundings with higher initial vertical uncertainty are given lower weight; soundings farther away are given lower weight; soundings with higher horizontal uncertainty cause the uncertainty to scale faster, and hence have lower weight. After the soundings are propagated to all nodes in their vicinity, each node has to determine how to assimilate them with the current state of knowledge about the depth in its location. The first stage is to run the soundings through a median ordered queue that implements a permutation of the normal input sequence to ensure that anomalously deep or shallow soundings are delayed before they go to the estimator proper (figure 4). Since the original sequencing of the soundings is arbitrary, this reordering does not change any

significant aspect of the remainder of the estimation, but it does significantly improve robustness by protecting the estimator until it learns about the true depth. The final stage is the estimator proper. CUBE utilizes an optimal Bayesian estimator described by a Dynamic Linear Model (DLM, [West & Harrison, 1997]). This estimator is causal and recursive, so that it can start making predictions as soon as the data starts to arrive, and only requires the current data estimate to assimilate the next data point. This is the basis of the real time implementation of CUBE, and ensures that we do not need to back-track into the data as each new point arrives. Each sounding that makes it into the estimator is weighed according to its propagated, combined uncertainty against the current state of knowledge of the depth at the node, represented by a depth estimate and measure of posterior variance. The weighting factor used balances the variances of the measurement and current estimate so that if the estimate is much more accurate than the measurement, it is only incrementally affected; if the measurement is very accurate, it will have a very significant effect (figure 5). After the current state is updated, the sounding is no longer required (all of the information implicit in it has been used) and hence it may be discarded; in implementation, it is retained in a backing database for further analysis. Model Monitoring and Intervention In CUBE, we have explicitly set up the model to indicate a constant depth. In practice, we observe that many soundings are not consistent with this hypothesis: outlier points violate this assumption by implying multiple alternative depths in the same location. Untreated, these points would corrupt the true depth estimate, provoking modeling failure. We use the error estimates of the soundings to provide a calibration point for model monitoring; that is, under the null hypothesis that the data is consistent with the model, the sounding and the current estimate should agree to within the sounding's predicted error. If they do not (to a statistically significant degree), then we may conclude that there is sufficient evidence to mistrust the sounding (figure 6). To make this system more useful, we must also observe long-term drifts (i.e., where the data and model drift apart slowly), and sequential failure, where the model is judged as being marginally inadequate for a significant number of samples. All of these may be implemented using the sequential Bayes factor monitoring of West & Harrison [1997]. After failure is indicated, our intervention scheme is to assume that the inconsistent sounding is another potential depth estimate, and to initialize another DLM to represent it. All models are maintained simultaneously and are treated equally until we are required to make a choice as to which one we believe to be the true depth. Maintaining a monotonically increasing list of models gives us some theoretical difficulty, since we have to determine against which model to compare the incoming sounding. We resolve this by choosing the model that is closest to the sounding in a least weighted error sense, with weighting function determined by the predicted error that would result were the sounding to be assimilated. Hence, if the model monitor indicates an outlier, we may safely build a new model track, since the sounding was compared to the best available model and found wanting (figure 7). Hypothesis Resolution Allowing multiple hypotheses provides robustness, but also ambiguity about which depth should be reported. CUBE implements a configurable disambiguation engine to choose a best hypothesis on demand, using predefined metrics on what constitutes best reconstruction. The simplest method chooses the hypothesis that has assimilated the most data points (i.e., which is best supported by the data). This works in most cases, although since it involves no context other than the data points, it can fail under significant noise content (e.g., if there are a burst of errors). Our second method finds neighboring nodes where there is only one hypothesis, and uses this certain reconstruction as a guide as to the probable true depth. Then, the hypothesis closest in depth to the guide node depth is used for reconstruction. The final method constructs context using another, potentially lower resolution, surface, constructed either from a previous survey or from the current one. Since this surface is only used as a guide to what the depth is, it does not have to be hydrographically correct and we can take more liberties with its compilation. For example, we can use a simple median bin at low resolution, or interpolate between smooth-sheet soundings from the previous survey, or even from the chart if no other information is available. As long as the surface is in approximately the correct location, it should help CUBE, on the average, work out which hypothesis is the correct one. Many other potential solutions exist, and are currently being researched.

Output Products In addition to the depth, CUBE is capable of providing additional metrics, in particular the uncertainty associated with the depth estimate, the number of hypotheses available at the node, and a measure of how certain the algorithm is about the choice of hypothesis that was made. Each of these is a scalar quantity, and hence may be represented as a surface, or more usefully as auxiliary information on top of another surface (figures 14-16). Combinations of these with the depth surface allow the user to see problems in context, and hence make decisions more reliably. The outputs of CUBEs processing are therefore a set of data vectors per node. It is natural to represent these as separate surfaces, but it is important to note that CUBEs estimates are strictly only estimates at a point, and any interpolation between those points must be considered separately. Remediation and Iteration It is unrealistic to expect that any algorithm will make the correct decision under all conditions. Therefore, it is imperative that there is an operator to check the decisions which have been made, and to rectify the problems evident in any area where CUBE either made no decision, indicates that the decision was in doubt, or made what the expert hydrographer believes to be the wrong decision, irrespective of the statistical distribution of data and noise. Our initial implementation uses the traditional data-flagging paradigm to assist CUBE in making decisions where the density of noise is such that the correct depth estimate is not evident to the algorithm. It is also potentially possible to work at the level of CUBEs hypotheses, or in a layered approach (e.g., edit hypotheses, and then data only if the problem is not resolved). After remediations are made, an iteration of CUBE is required to integrate the modifications with the rest of the data. CUBE is, in this sense, a one-way trapdoor: once the soundings have been assimilated into the estimates, there is no way to back them out except to start again. However, the speed of the algorithm is such that this is not a significant concern. In practice, since the processing is mainly local, we need not rerun the algorithm everywhere just in regions where modifications have been made. This significantly reduces the computational burden, particularly when there number of modifications is expected to be small. Implementation We have avoided, whenever possible, redeveloping tools that are available in COTS software, preferring to interface to available applications for data reformatting, display and manipulation. The essential support requirements for a host system are that it should have an API for data retrieval, preferably a spatially based one (i.e., that can provide all data within a given radius of a particular point). It should also contain a manipulation system for data so that remediations can be done, and a suitable display system that is capable of displaying multiple surfaces simultaneously. No one system currently available has all of these, so we have built a hybrid system using CARIS/HIPS for data conversion, manipulation and display, GeoZui3D for fast turn-around display of multiple surfaces with overlaid color-coded data, and Fledermaus/PFM for advanced visualization, spatially-indexed data retrieval and area-based editing. A combination of bash shell scripts, perl and the GMT package are also used in development and implementation of the various stages of the algorithm and product preparation. The CUBE process occurs in two passes when used in post-processing mode. The first pass (figure 8) generates preliminary surfaces for the user to examine; the second pass (figure 9) takes any user modifications and generates final product surfaces. We read directly from HDCS data using the HIPS/IO interface libraries, and store CUBEs results in a specialist data structure called a MapSheet (SHT). This intermediate store provides extra flexibility, and allows us to maintain state between data availability. From the MapSheet, we can generate both HIPS Weighted Grids (HWGs) and GeoZui3D GUTMs. The HWGs are inserted back into a HIPS Fieldsheet, so that they can be seen in conjunction with the raw data; we typically attempt to display the HWGs and data on one screen of a dual-monitor system, and the GUTMs on the other. We have found that it is significantly easier to manipulate data if both representations of the data are available simultaneously, since it is difficult to fuse the information mentally in many cases, and cumbersome to transfer by hand the information from the 3D visualization, where problems are obvious, to the manipulation system where they can be rectified. It is our experience that getting the implementation of this coupling correct can significantly affect the ease-of-use of a system and hence the potential benefits that can be achieved. In real-time mode, it is not sufficient to have this once-through model of processing, since we want to be able to work data incrementally as it is being gathered, typically on a daily cycle. We currently resolve this by maintaining two MapSheet structures, one for daily use and one for cumulative use. At the start

of each day, the cumulative MapSheets are used to initialize the daily set, and the current days data is then assimilated. Once any changes to the data have been made based on the intermediate results, the second pass of CUBE is used to assimilate the days data into the cumulative MapSheets. In this way, the cumulative MapSheets should always represent the best available information on the survey. Working in this incremental mode saves considerable time in processing, although the cumulative MapSheets can always be re-constructed at any time simply by re-running the data from the start.

Example: Woods Hole, MA.

During the 2001 field season, the NOAA Ship WHITING conducted hydrographic survey operations around Cape Cod, including Woods Hole, MA (4131'N 7040'W, registry number H11077), from Great Harbor to Vineyard Sound, figure 10. Over approximately five survey days, the WHITINGs multibeam survey launch covered approximately 1.7km2 in depths from 2m to 30m with full coverage from a Reson 8101 MBES. A POS/MV 320 was used for attitude measurement and positioning was derived from a Trimble DSM212 differential GPS receiver (corrections: Chatham, MA). All of the data was archived in XTF format and then converted into CARIS/HIPS for processing. Corrections for static and dynamic offsets, refraction and tides were made, and the resultant HDCS data was provided as the starting point for CUBEs processing. The data archive contained edit flags, but these were removed from the test set before starting automatic processing. We used a depth gate of (2,30)m to avoid gross outliers, although we allowed all beams to be used rather than applying the standard angle gate of 60 per the Data Acquisition and Processing Report (DAPR) for the survey [Glang et al., 2001]. This provided more coverage in very shallow areas hence allowing for a more stable reconstruction, although we did encounter more multiple hypothesis areas because of this decision, and hence have taken more time to work the data than we otherwise might. We bootstrapped analysis of the data by constructing a 5m median bin using all of the data. This is inadequate for hydrography, but provides a suitable reference for slope corrections and dynamic depth ranges. We utilized a blunder filter to remove any soundings more than 25% deeper than the median estimated depth (with a minimum depth difference of 1m), and then processed all of the data at 0.5m resolution in order to ensure that small shoals are reliably estimated, and to provide the highest possible resolution surface for the area. The resultant surface was inspected and remediations made by flagging the original soundings. The CUBE algorithm was then iterated to complete the processing. The non-interactive processing took approximately 60 min. per pass on commodity PC hardware; the interactive time was approximately 240 min., although much of that time was spent investigating the many small lumps in the harbor area rather than actually editing data. It is important to note that the robustness of CUBE's estimation algorithms allows us to be a little more cavalier about editing, in the sense that we do not have to remove every single anomalous sounding in the set, simply enough to give CUBE a head start in estimating the surface, i.e., to improve the signal-to-noise ratio. Therefore, we may remove just the obvious outliers, and allow the algorithm to process those close to the 'true' surface appropriately. This was used to preserve the objectivity of CUBEs estimates. We found that the majority of the data was processed automatically, and the level of detail in the results is high (figures 11-12). Preservation of detail is an important concern in automatic processing schemes since an over-zealous procedure could also remove important small features. The dynamic depth gate implemented by the blunder filter bootstrapped by the median depth significantly improves performance in sparse areas for little extra cost, although this affects only deep spikes. We observed a number of small trackline oriented holidays in the data (figure 13), which were subsequently tracked to dropped packets in the input data stream (i.e., missing data not recorded by the capture system). Although these holidays are not significant with respect to hydrographic coverage of the area, they illustrate a problem with current data processing methods. There is no way to detect these dropped packets without investigating the timestamp on each packet of input data, which is obviously unfeasible, and they are not immediately obvious in points-mode data displays. To demonstrate coverage, only grids at approximately 5m resolution are required, and under any conventional grid construction scheme, these sorts of holidays would not be observed. Here, CUBE has been able to illustrate a potential problem, and provides a way to visualize them so that reasoned quantitative decisions can be made (in this case, to ignore the holidays as hydrographically insignificant). A use for the number of self-consistent hypotheses is illustrated through the data around the Woods Hole Oceanographic Institution (WHOI) dock. The dock pilings are sufficiently large to return multiple beam

hits, and hence CUBE resolves multiple hypotheses, as seen in figure 14. The obvious geometric arrangement of the multiple hypotheses clearly indicates that these are man-made, although this is not immediately obvious just from the surface, since it is constrained to choose just one hypothesis as best. An objective measure of consistency such as this is a very powerful tool in making decisions about what to keep, and what to ignore. CUBE also provides uncertainty estimates (figure 15) that provide information about the quality of the chosen hypothesis, and a measure of hypothesis strength (figure 16) that attempts to measure how sure the algorithm is about the choice of hypothesis that it made. Use of these indicators can further inform processing to best utilize operator time. To compare the CUBE output with a traditional hydrographic processing chain, we took the preliminary smooth-sheet selected soundings for the survey, and matched them against the CUBE surface, assuming that they are IHO Order 1 accurate (the target for the survey) [IHO, 1996]. For each selected sounding, we found the reconstructed CUBE depth within the horizontal 95% CI for the sounding that minimized the absolute vertical difference between sounding and surface. We then scaled this difference by the vertical 95% CI for the sounding and computed the cumulative probability mass function over the 5902 selected soundings (figure 17). We observe that just over 95% of the soundings are below the one unit CI limit (135 soundings of 5902, or 2.3% are above) as expected, showing that the CUBE surface agrees very well with the traditional selected sounding approach in this case. The slight bias is probably due to a combination of finite sample effects and the traditional approach of shoal biased selection of soundings.

Conclusions

Our current methods of processing Multibeam Echosounder (MBES) data are becoming inadequate as faster and higher resolution systems come online. We have argued that statistical methods of processing data are not only useful, but are in fact required when we consider the properties of MBES data. We have outlined an alternative method for processing such data, which attempts to handle the majority of soundings automatically by focusing on estimation of true depth, rather than selecting best soundings, while building in quantitative estimates of data quality and guideline metrics for QA/QC. We accept that no method will be completely automatic. We have therefore also outlined an inspection and feedback mechanism that attempts to harness the power of automatic methods to bootstrap operator effort. The algorithm can be run in once-through (batch) or real-time mode, and can provide interim results as data is being gathered. Through the data example shown here, we have illustrated the CUBE algorithm. We observe that the algorithm is suitably robust for typical hydrographic systems, and that it handles the majority of data automatically; the algorithm is also sufficiently fast to keep up with data capture rates, even in an experimental research implementation. We have found that the algorithm is not sensitive in its parameters (given calibration of the error model through installation and patch test measurements), so that it does not need to be retuned for each dataset. We have shown elsewhere [Calder & Mayer, 2001, 2002] that CUBEs estimates are statistically equivalent to more conventional surface estimation techniques, and here that they agree well with a traditionally constructed selected sounding set. We are currently pursuing a project to show hydrographic equivalence (in the sense that the same hydrographic conclusions would be reached using CUBEs results as for a traditional processing scheme).

References

Calder, B. R., and L. A. Mayer, Robust Automatic Multibeam Bathymetric Processing, Proc. U.S. Hydro. Conf. 2001, Norfolk, VA, 2001 (reprints: www.thosa.org/us01papers.htm). Calder, B. R., and L. A. Mayer, Automatic Processing of High-Rate, High-Density Multibeam Echosounder Data, submitted to Geochem., Geophys., Geosyst. (G3, gcubed.org), DID 2002GC00486, December 2002. Debese, N., Use of a Robust Estimator for Automatic Detection of Isolated Errors Appearing in Bathymetry Data, Int. Hydro. Review, 2(2), 32-44, 2001. Debese, N. and P. Michaux, Dtection Automatique dErreurs Ponctuelles Prsentes dan les Donnes Bathymtriques Multifaisceaux Petits Fonds, Proc. Canadian Hydro. Conf. 2002, Toronto, 2002. Du, Z., D. E. Wells, and L. A. Mayer, An Approach to Automatic Detection of Outliers in Multibeam Echosounding Data, The Hydro. Journal, 79, 19-25, 1996.
Eeg, J., On the Identification of Spikes in Soundings, Int. Hydro. Review, 72(1), 33-41, 1995. Glang, G., M. Cisternelli, and R. Brennan, NOAA Ship WHITING Data Acquisition and Processing Report S-B904-WH (Woods Hole, MA; registry number H11077), National Ocean Service, NOAA, 2001. Gourley, M., and K. DesRoches, Clever Uses of Tiling in CARIS/HIPS, Proc. 2nd Int. Conf. on High Resolution Survey in Shallow Water, Portsmouth, NH, September 2001. Hare, R., A. Godin and L. A. Mayer, Accuracy Estimation of Canadian Swath (Multibeam) and Sweep (Multitransducer) Sounding Systems, Tech Rep., Canadian Hydrographic Service, 1995. IHO Committee, IHO Standard for Hydrographic Surveys, Int. Hydro. Organization, Special Publication S.44, 4ed, 1996. Smith, S., L. Alexander, and A. Armstrong, The Navigation Surface: A New Database Approach to Creating Multiple Products from High-Density Surveys, Int. Hydro. Review, 3(2), August 2002. Ware, C., L. Slipp, K. W. Wong, B. Nickerson, D. E. Wells, Y. C. Lee, D. Dodd, and G. Costello, A System for Cleaning High Volume Bathymetry, Int. Hydro. Review, 69(2), 77-94, 1992. West, M., and J. Harrison, Bayesian Forecasting and Dynamic Models, 2 ed., Springer-Verlag, 1997.

Acknowledgements

The support of NOAA grant NA97OG0241 is gratefully acknowledged, as are the many fruitful discussions I have had with colleagues, and skeptical hydrographers, who kept the process grounded in something like reality. My thanks also to the Captain and crew of the NOAA Ship WHITING for the provision of, and their assistance with, the dataset presented. Note that the use of particular software or hardware in the description of this work is not intended as endorsement. Trademarks and copyrights of the respective manufacturers are acknowledged, even if not so marked in the text.

Predicted Vertical Error

Predicted Horizontal Error
0.3 0.28 0.26 0.24 95% CI (m) 0.22 0.2 0.18 0.16 0.14 0.12 0.1 -75 -50 -Angle off Nadir (deg) 75 -75 -50 -25 1.Angle off Nadir (deg) 75 1.8 95% CI (m) 2.4 2.2.6

Figure 1: Typical error performance of an MBES system in shallow water. These graphs show performance for a typical MBES on a small survey launch using differential GPS for basic positioning and a high-accuracy attitude sensor. Target depth is 25m.
Figure 2: Propagation of information. Estimation at a point implies that we need to know the depth there; soundings, however, occur essentially at random. Hence, we must propagate the information to the location of the estimation nodes, taking care to model an increase in uncertainty associated with the fact that we are using the sounding at some distance from the nominal location.
Figure 3: Uncertainty in propagation. The uncertainty associated with a sounding must increase the further we move from the nominal resolved location; in this case, it is modeled as a quadratic function of distance. Horizontal uncertainty is taken into account by assuming that the sounding may be up to the maximum likely distance away, rather than at the nominal distance. The difference is a linear function of the estimated horizontal uncertainty.
1 -8 -9 -10 Depth (m) -11 -12 -13 -14 -15

Input Output

Sample sequence
Figure 4: Permutation of input soundings. Since the ordering of data is not important in CUBE, we can re-sequence the inputs before they reach the Bayesian estimator in order to delay what appear to be outlier points. This is implemented using a moving median window, which delays any soundings that are shoaler or deeper than the rest of the data in the window.
Figure 5: Update procedure at a node with a single depth hypothesis. The current estimate is updated with the information implicit in the new sounding. Since the new sounding is believed to be less accurate than the current estimate (i.e., has higher variance), the updated estimate is mostly determined by the current estimate.
Figure 6: Model monitoring scheme. CUBE predicts that the next depth will be the same as the current estimate, and then uses this as the null hypothesis to test the incoming data (against a simple alternative of a step change in depth). If the null hypothesis cannot be rejected, the Bayesian data assimilation takes place. Otherwise a new depth tack is started.
Figure 7: Model selection for monitoring and test assimilation. Use of a minimum predicted error distance ensures that the best model is chosen, and hence that if the data is found to be inconsistent (see figure 6), then we can start another depth track since no other model would choose to assimilate the data either.
Figure 8: First-pass flow diagram for CUBE processing. We interface to HDCS data so that all normal CARIS/HIPS tools are still available, although for flexibility, we use a separate visualization suite to display the data, and do the remediation in spatial mode of HIPS 5.2.
Figure 9: Second-pass flow diagram for CUBE processing. This is essentially the same as the first pass, except that we move directly to products from the MapSheet (SHT) database through automatic methods, rather than through some intermediate cartographic extraction. A more detailed description of this process is outlined in Smith et al. [2002].

Figure 10: Woods Hole, MA (H11077), conducted by the NOAA Ship WHITING, 2001 [Glang et al., 2001]. Both chart and data are reprojected to UTM Zone 19N, WGS-84 ellipsoid. Depth range is (2,30) m, and coverage is approximately 1.7km2.
Figure 11: Reconstructed bathymetry in southwest corner of Woods Hole data, looking west. The main sand ripples have amplitude approx. 0.5m, and wavelength approx. 10m, although they are overlaid with sand ripples of smaller wavelength and amplitude. The rougher texture to the right is though to be a dumping area overlaid on the sand ripples.
Figure 12: Man-made objects. Thought to be the remains of a floating dock and a floatplane, these objects occur in the northwest corner of the survey, just west of the WHOI dock. The many small features on the area around the dock are probably mooring blocks or rocks.
Figure 13: Example of track-line oriented artifacts that are only obvious at high resolution, but which are symptomatic of a problem with the data acquisition system. Feedback like this from CUBEs outputs as the survey progresses could help with the early detection and remediation of such problems in the field, where the cost of correction is significantly less.
Figure 14: Number of hypotheses at each estimation node color-coded over the reconstructed bathymetry; hot colors indicate more self-consistent hypotheses were formed. From the pattern of hypothesis clusters, it is immediately obvious that these were caused by pilings for the associated dock structure. This is not obvious from the bathymetry alone.
Figure 15: Uncertainty color-coded over bathymetry; view from Great Ledge looking north to Woods Hole passage. The color-coding is 95% confidence interval predicted from the posterior variance of the depth estimate chosen by the disambiguation engine, with warmer colors indicating higher uncertainty. Prediction variance is a function of the number of soundings assimilated and their component uncertainties. The primary signals evident here are depth range and beam angle, shown in the linear features derived from the line-plan used during the survey.
Figure 16: Hypothesis strength color-coded over reconstructed bathymetry; WHOI dock looking north. Hypothesis strength is a metric indicating how certain the disambiguation engine is about the hypothesis it reported. Green indicates strong evidence for the chosen hypothesis; the scale to red indicates decreasing evidence, implying that there are other plausible solutions.

1.Cumulative Probability Mass 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.0 0.1.2.3.5 Proportion of Vertical Error Limit
Figure 17: Cumulative probability mass function for comparison between preliminary smooth-sheet selected soundings and CUBE output surfaces. The horizontal scale is minimum vertical difference between the CUBE surface and the selected sounding assuming that the soundings are IHO Order 1 accurate (the target for the survey). The axis is scaled to the vertical 95% CI for IHO Order 1 survey, so we expect (and observe) 95% of the selected soundings with vertical error less than 1.0.