Broadband fish identification of great lake fishes
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Broadband Fish Identification of Great Lake Fishes. Patrick Simpson and Mike Tuohey Scientific Fishery Systems, Inc. Anchorage, AK Guy Fleischer and Ray Argyle Biological Resources Division - U.S. Geological Survey Great Lakes Science Center Ann Arbor, MI. Overview. Why Broadband Sonar?

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Broadband Fish Identification of Great Lake Fishes

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Broadband Fish Identification of Great Lake Fishes

Patrick Simpson and Mike Tuohey

Scientific Fishery Systems, Inc.

Anchorage, AK

Guy Fleischer and Ray Argyle

Biological Resources Division - U.S. Geological Survey

Great Lakes Science Center

Ann Arbor, MI


Overview

  • Why Broadband Sonar?

  • System Overview

  • Data Collection

  • Classification Results

  • Future Work


Broadband vs. Narrowband


Benefits of Broadband Fish Identification

  • Full-Column Assessment

  • Continuous Assessment

  • Remote Assessment

  • Cost Savings


Broadband Sonar Fish Identification System Prototype


Broadband Transducer

  • Resonant Frequency 153,600 Hz

  • 3 dB Operation Band45 kHz (138 - 183 kHz) Q=3.4

  • Active Surface177-mm disc

  • Beam Pattern4.1° beamwidth, sidelobes @ 20 dB

  • Rated Power80 W transducer & wet electronics

  • Source Level216 dB re 1 uPa @ 1 m @ 153.6 kHz

  • Transmit Sensitivitypeak TVR of 181 dB re 1 uPa-m/V @ 169 kHz

  • Receive Sensitivitypeak OCVR of -180 dB re 1 V/uPa @ 169 kHz


Processing Platform

  • Mid-tower computer case with 230 W power supply

  • Plato motherboard with ISA/PCI bus adapters, 256 kByte cache

  • Intel Pentium 90 MHz CPU with 16 Mbyte DRAM

  • PCI SCSI-2 host adapter controlling three SCSI-2 devices below

  • 1.0 GB hard disk, 1.3 GB magneto-optical drive, and quad-speed SCSI-2 CD-ROM

  • 12 bit 770 kS/s ADC/DSP card with 486DX2/66 and 4 MB on-board DRAM

  • 1280 x 1024 video monitor and video adapter card with 2 MB DRAM

  • DOS 6.22 running ORCA.EXE Interface & Processing


Data Processing

A/D

CONVERSION

ECHO

DETECTION

ACOUSTIC RECEIVER

FEATURE EXTRACTION

MAN-MACHINE

INTERFACE

CLASSIFICATION

SIGNATURE

DATABASE


Features

  • Single Ping Parameters

  • Multiple Ping Parameters

  • Environmental Parameters


Features

Input

a

a

a

a

o o o

F

1

n

3

2

A

Nodes

Hidden

b

b

b

b

o o o

F

Nodes

p

3

2

1

B

Output

c

c

c

c

o o o

Nodes

F

m

3

2

1

C

Classes

Neural Net Classifier


Tethered Individual Fish


Free-Swimming Fish


Tethered Data Collection


Classification Results


Tethered Vs. Free-Swimming Results


Future Work

  • Wider Beam (15 degrees)

  • Dual / Split Beam

  • Greater Source Level

  • Streamline Data Storage and Analysis

  • More Data Collection


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