A lucky covariance estimator based on cumulative coherence

Area of Science:

• Acoustics
• Signal Processing
• Array Signal Processing

Background:

• Adaptive acoustic localization performance relies heavily on accurate covariance matrix estimation.
• Existing methods for covariance estimation can be sensitive to noise and data quality.

Purpose of the Study:

• To introduce and evaluate a novel technique for improving covariance estimation in adaptive acoustic localization.
• To leverage principles from lucky signal processing and cumulative coherence for enhanced data quality.

Main Methods:

• Applied lucky signal processing, selecting high-quality snapshots based on cumulative coherence.
• Generated dense snapshots with high overlap from acoustic array data.
• Compared the novel lucky covariance estimator against standard methods using SWellEX-96 experimental data.

Main Results:

• The lucky covariance estimate demonstrated success in adaptive matched field processing.
• The new estimator produced less ambiguous processor outputs compared to conventional methods.
• A higher estimated signal-to-noise ratio was achieved, particularly at longer source ranges.

Conclusions:

• Lucky signal processing combined with cumulative coherence offers a significant improvement for covariance estimation in acoustic localization.
• This technique enhances the performance of adaptive matched field processing, especially in challenging long-range scenarios.
• The proposed method provides a more robust and accurate approach to acoustic array data analysis.