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Christopher O. Tiemann Michael B. Porter Science Applications International Corporation John A. Hildebrand Scripps Institution of Oceanography Automated.

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Presentation on theme: "Christopher O. Tiemann Michael B. Porter Science Applications International Corporation John A. Hildebrand Scripps Institution of Oceanography Automated."— Presentation transcript:

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

2 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

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

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

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

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

7 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

8 1)Pixilate spectrograms to binary intensity (black & white) Spectrogram Correlation 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

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

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 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

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

13 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

14 Whale movement can be followed with time-lapse movies. Click on a figure to play. San Clemente 08/28/01 02:52 – 04:43San 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 UTM East (km) UTM North (km) Sperm whale localization at PMRF 03/10/02 11: m depth 800 m depth

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 Time ( sec ) Amplitude PMRF receiver 501 waveform, 03/10/02 11:52, with clicks identified

17 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

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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