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