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Automated Model-Based Localization of Marine Mammals

This study presents a model-based localization technique for marine mammals using acoustic data. The algorithm provides accurate and robust localization results, applicable to sparse arrays, with real-time processing capabilities. The method has been tested with real acoustic data from different locations and species.

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Automated Model-Based Localization of Marine Mammals

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  1. Automated Model-Based Localization of Marine Mammals Christopher O. Tiemann Michael B. Porter Science Applications International Corporation John A. Hildebrand Scripps Institution of Oceanography

  2. Traditional Passive Acoustic Localization Methods • Hyperbolic fixing –Assumption of direct acoustic path • and constant soundspeed • Matched-field processing –Sensitive to environment • 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

  3. Algorithm has been tested with real acoustic data from two locations Robust against differences in environment and species PMRF Deep water Humpback whale calls .2-4 kHz 2 sec duration Sperm whale clicks Hydrophone array San Clemente Shallow water Blue whale calls 10-20 Hz 20 sec duration Seismometer array

  4. Array Geometries Pacific Missile Range Facility Hydrophone Positions San Clemente Seismometer Positions

  5. Spectrograms from PMRF Channels 2 and 4 3/22/01 20:16:30 dB Time-Lag dB

  6. San Clemente Seismometer Spectrograms Seismometer #1 08/28/01 11:36 Sensors measured 3-axis velocity plus pressure Blue whale type ‘A’ and ‘B’ calls observed 4 receivers 11 days of data 128 Hz sample rate

  7. Algorithm Overview 1) Predict direct and reflected acoustic path travel times and time-lags 2) Pair-wise cross- correlation measures time-lag 3) Compare predicted vs measured time-lags for likelihood scores 4) Summed scores form ambiguity surface indicating mammal position and confidence

  8. Time-lag between Ch. 2 & 4,3/22/01 20:16:00 3) Maximum correlation score determines time-lag Ch. 2,3/22/01 20:16:30 Spectrogram Correlation • Pixilate spectrograms • to binary intensity • (black & white) Ch. 4,3/22/01 20:16:30 2) Correlate via logical AND and count of overlapping pixels

  9. Spectral correlations provide more consistent time-lag estimates than do waveform correlations Time-lag between PMRF Ch. 2 & 4,3/22/01 20:16:00 Time-lag between PMRF Ch. 2 & 4,3/22/01 20:16:00

  10. 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

  11. Ambiguity Surface Construction PMRF 3/22/01 20:16 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

  12. Whale Tracking Ambiguity surface peaks from consecutive localizations follow movement of source San Clemente

  13. Tracking Examples • Sources can be localized far outside array • Tracks give clues to animal behavior 08/29/01 02:55-04:50 08/28/01 02:52-04:52 08/28/01 09:33-13:50

  14. Tracking Examples Whale 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

  15. Depth Estimation Repeat modeling and surface construction for several depths Surface peak defocuses at incorrect depths Sperm whale localization at PMRF 03/10/02 11:53 800 m depth 200 m depth UTM North (km) UTM East (km) UTM East (km)

  16. Multiple Sources • Singing 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 PMRF receiver 501 waveform, 03/10/02 11:52, with clicks identified Amplitude Time (sec)

  17. Verification • Goal to verify accuracy of localization algorithm • Low probability of concurrent visual and acoustic localization • of same individual Sperm Whale Localizations at PMRF 03/10/02 • Matchedacoustics to • visual sighting • of sperm whale pod • at PMRF • Have data from • controlled-source • localization • experiment at AUTEC 11:54-11:56 11:55 11:58 11:53-11:56

  18. Conclusions • Model-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

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