Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the...

Area of Science:

• Underwater acoustics
• Signal processing
• Array signal processing

Background:

• Passive multiple sound source localization is difficult with short hydrophone arrays in deep oceans.
• Compressive sensing (CS) techniques have been explored to address this challenge.

Purpose of the Study:

• To develop and apply a novel greedy algorithm combined with a spatial filter for two-source localization in deep ocean environments.
• To evaluate the performance improvement of the proposed method compared to existing techniques.

Main Methods:

• A greedy algorithm from CS theory was integrated with a spatial filter.
• The combined method was applied iteratively for source localization.
• Simulated and experimental data were used for validation.

Main Results:

• The proposed method demonstrated improved localization performance compared to the Bartlett processor and a greedy algorithm without a spatial filter.
• The study analyzed the impact of array aperture, number of hydrophones, snapshots, and signal-to-noise ratio (SNR) on localization accuracy.

Conclusions:

• The developed greedy algorithm with a spatial filter offers a viable solution for passive multiple sound source localization in challenging underwater conditions.
• The findings provide insights into optimizing array configurations and signal conditions for enhanced localization accuracy.