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![Page 1: Christopher O. Tiemann Michael B. Porter Science Applications International Corporation John A. Hildebrand Scripps Institution of Oceanography Automated.](https://reader033.fdocuments.in/reader033/viewer/2022061306/551468f95503462d4e8b5c8e/html5/thumbnails/1.jpg)
Christopher O. Tiemann
Michael B. PorterScience Applications International Corporation
John A. HildebrandScripps Institution of Oceanography
Automated Model-Based Localizationof Marine Mammals
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Advantages of Model-Based Localization Technique
• Acoustic propagation model provides accuracy
• Robust against environmental and acoustic variability
• Graphical display with inherent confidence metrics
• Applicable to sparse arrays
• Fast for real-time processing without user interaction
• Hyperbolic fixing – Assumption of direct acoustic path and constant soundspeed
• Matched-field processing – Sensitive to environment
Traditional Passive Acoustic Localization Methods
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Algorithm has been tested with real acoustic data from two locations
PMRFDeep waterHumpback whale calls .2-4 kHz 2 sec durationSperm whale clicksHydrophone array
San ClementeShallow waterBlue whale calls 10-20 Hz 20 sec duration
Seismometer array
Robust against differences in environment and species
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Pacific Missile Range FacilityHydrophone Positions
San ClementeSeismometer Positions
Array Geometries
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Time-Lag
dB
dB
Spectrograms from PMRF Channels 2 and 4
3/22/01 20:16:30
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San Clemente Seismometer Spectrograms
4 receivers11 days of data128 Hz sample rate
Blue whale type ‘A’ and ‘B’ calls observed
Sensors measured 3-axis velocityplus pressure
Seismometer #1 08/28/01 11:36
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3) Compare predicted vs measured time-lags for likelihood scores
Algorithm Overview
1) Predict direct and reflected acoustic path travel times and time-lags
2) Pair-wise cross- correlation measures time-lag
4) Summed scores form ambiguity surface indicating mammal position and confidence
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1) Pixilate spectrograms to binary intensity (black & white)
SpectrogramCorrelation
Ch. 2, 3/22/01 20:16:30
Ch. 4, 3/22/01 20:16:30
2) Correlate via logical AND and count of overlapping pixels
Time-lag between Ch. 2 & 4, 3/22/01 20:16:00
3) Maximum correlation score determines time-lag
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Time-lag between PMRF Ch. 2 & 4, 3/22/01 20:16:00Time-lag between PMRF Ch. 2 & 4, 3/22/01 20:16:00
Spectral correlations provide more consistent time-lag estimates than do waveform correlations
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Phase-Only Correlation• Measures time-lag between receiver pairs• Product of two whitened spectra• Frequency-band specific• Advantages over waveform or spectrogram correlation• Over time, see change in bearing to persistent sources
Pair-wise Time-lag between Seismometers #1 and #4 08/28/01 – 08/30/01
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1) Discard low-score time-lags
2) Compare predicted vs measured time-lags for all candidate source positions
3) Sum likelihood contributions from all hydrophone pairs
Ambiguity Surface Construction
PMRF 3/22/01 20:16
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Whale TrackingAmbiguity surface peaks from consecutive localizations follow movement of source
San Clemente
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• Sources can be localized far outside array• Tracks give clues to animal behavior
08/28/01 02:52-04:52 08/28/01 09:33-13:50 08/29/01 02:55-04:50
Tracking Examples
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Tracking ExamplesWhale movement can be followed with time-lapse movies.
Click on a figure to play.
San Clemente 08/28/01 02:52 – 04:43 San Clemente 08/28/01 09:33 – 13:50
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Depth Estimation
Repeat modeling and surface construction for several depths
Surface peak defocuses at incorrect depths
UTM East (km)UTM East (km)
UT
M N
orth
(km
)
Sperm whale localization at PMRF 03/10/02 11:53
200 m depth 800 m depth
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Multiple SourcesSinging whales
• Time-lag from single correlation peak limits one localization per receiver pair• Different receiver pairs can localize different sources on same ambiguity surface
Clicking whales• Pair-wise click association tool measures time-lag• Can track multiple whales simultaneously
Time (sec)
Am
plitu
de
PMRF receiver 501 waveform, 03/10/02 11:52, with clicks identified
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Verification• Goal to verify accuracy of localization algorithm
• Low probability of concurrent visual and acoustic localization of same individual
• Matched acoustics to visual sighting of sperm whale pod at PMRF
• Have data from controlled-source localization experiment at AUTEC
Sperm Whale Localizations at PMRF 03/10/02
11:53-11:56
11:54-11:56
11:55
11:58
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ConclusionsModel-based algorithm benefits:
• Portable to other distributed array shapes, environments, and sources of interest• Robust against environmental variability• Suitable for automated real-time processing• Modular design
Future work:• Test on other ranges, species and vs. controlled source• Add species identification tool• Long-term, real-time range monitoring and alert generation