Direction of arrival estimation by Perona & Malik regularized Richardson-Lucy deconvolution

Area of Science:

• Underwater acoustics
• Signal processing
• Array signal processing

Background:

• The Richardson-Lucy (RL) deconvolution algorithm is crucial for direction of arrival (DOA) estimation in underwater acoustic arrays.
• RL algorithm's performance degrades at low signal-to-noise ratios (SNR) due to the ill-posed nature of inverse problems.

Purpose of the Study:

• To enhance the performance of the RL algorithm for DOA estimation in challenging underwater acoustic environments.
• To mitigate the degradation of RL algorithm performance at low SNRs.

Main Methods:

• Proposed a novel approach combining the Richardson-Lucy (RL) algorithm with the Perona & Malik regularizer.
• The Perona & Malik regularizer was employed to smooth beam sidelobes and sharpen target peaks, constraining the solution space.
• Performance was evaluated against conventional beamforming, the original RL algorithm, and a total variation regularized RL method using simulated and experimental data.

Main Results:

• The proposed RL with Perona & Malik regularizer demonstrated superior performance compared to other methods.
• Achieved significant reduction in beam width and improved multi-target resolution.
• Successfully lowered sidelobe levels, particularly at signal-to-noise ratios above -10 dB.

Conclusions:

• The integration of the Perona & Malik regularizer effectively enhances the RL algorithm for underwater acoustic DOA estimation.
• The proposed method offers a robust solution for improving resolution and reducing sidelobe levels in low SNR environments.
• This advancement is critical for accurate target localization in complex underwater acoustic scenarios.