Passive Source Localization Using Compressive Sensing

Area of Science:

• Acoustics
• Signal Processing
• Oceanography

Background:

• Underwater passive source localization is crucial for marine applications.
• Existing methods face challenges with accuracy and computational load.

Purpose of the Study:

• To develop an efficient and accurate underwater passive source localization method.
• To improve upon existing sparse reconstruction techniques for acoustic signals.

Main Methods:

• Formulating an underdetermined linear inversion problem for source localization.
• Employing sparse reconstruction algorithms to exploit spatial signal sparsity.
• Utilizing a data correlation matrix with multiple snapshots to reduce dimensionality.
• Incorporating Bartlett pre-location to decrease computational burden.

Main Results:

• Achieved a high ratio of measurements to sparsity (RMS).
• Demonstrated increased peak sharpness and reduced sidelobe levels.
• Showcased comparable performance to Bartlett and white noise gain constraint methods in single-source scenarios.
• Exhibited superior performance in localizing multiple sources.
• Validated effectiveness with both simulated and experimental underwater data.

Conclusions:

• The proposed method offers a robust and high-resolution approach for underwater passive source localization.
• It effectively handles environmental/system mismatches, similar to established techniques.
• The technique provides a valuable advancement for identifying underwater acoustic sources.