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Transcript of Christopher O. Tiemann Michael B. Porter Science Applications International Corporation John A....
Christopher O. Tiemann
Michael B. PorterScience Applications International Corporation
John A. HildebrandScripps Institution of Oceanography
Automated Model-Based Localizationof Marine Mammals
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
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
Pacific Missile Range FacilityHydrophone Positions
San ClementeSeismometer Positions
Array Geometries
Time-Lag
dB
dB
Spectrograms from PMRF Channels 2 and 4
3/22/01 20:16:30
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
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
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
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
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
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
Whale TrackingAmbiguity surface peaks from consecutive localizations follow movement of source
San Clemente
• 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
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
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
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
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
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