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Performance comparisons of array invariant and matched field processing using broadband ship noise and a tilted

Gihoon Byun¹, H C Song¹, J S Kim²

¹Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA.

The Journal of the Acoustical Society of America

|January 3, 2019

Summary

Array invariant (AI) and matched field processing (MFP) offer distinct approaches to underwater acoustic source localization. AI excels with minimal environmental data, while MFP requires precise environmental knowledge for comparable performance.

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Area of Science:

Underwater acoustics
Signal processing
Geophysics

Background:

Accurate underwater acoustic source localization is crucial for maritime surveillance and research.
Traditional methods like Matched Field Processing (MFP) are sensitive to environmental parameters and array geometry.
Array Invariant (AI) offers a deterministic, environmentally-robust alternative for source localization.

Purpose of the Study:

To compare the localization performance of Array Invariant (AI) and Matched Field Processing (MFP).
To evaluate the impact of array tilt on both AI and MFP.
To assess the potential for AI to improve MFP performance.

Main Methods:

Utilized a ship of opportunity emitting broadband noise (200-900 Hz) and a tilted vertical array.
Employed AI's beam-time migration for deterministic range estimation, exploiting signal dispersion.
Applied MFP using generated replicas based on environmental models and array geometry.

Main Results:

Both AI and MFP are sensitive to array tilt.
AI demonstrated self-calibration capability for simultaneous array tilt and source range estimation.
MFP performance approached AI's level when incorporating AI-derived tilt information and accurate environmental data.

Conclusions:

AI provides robust source range estimation with minimal environmental assumptions.
Accurate environmental knowledge and array tilt compensation are critical for high-performance MFP.
AI can enhance MFP by providing essential calibration parameters, improving overall localization accuracy.