Vector Hydrophone Array Design Based on Off-Grid Compressed Sensing.

Area of Science:

• Underwater acoustics
• Array signal processing
• Optimization algorithms

Background:

• Sparse array design is crucial but challenging in array signal processing.
• Vector hydrophone arrays offer richer information than scalar arrays in underwater environments.
• Simultaneous optimization of multiple channels in vector hydrophones is a key design challenge.

Purpose of the Study:

• To develop an effective method for sparse vector hydrophone array design.
• To address the challenge of simultaneous multi-channel optimization in vector hydrophones.
• To improve array performance using compressed sensing (CS) theory.

Main Methods:

• Introduced compressed sensing (CS) theory into vector hydrophone array design.
• Formulated the design problem as a globally solvable optimization problem.
• Proposed a CS-based algorithm utilizing the L1 norm and an off-grid approach for enhanced accuracy.

Main Results:

• The proposed algorithm enables simultaneous optimization of multiple channels from the same vector hydrophone.
• Achieved higher design accuracy with the addition of an off-grid algorithm.
• Demonstrated superior performance in beam pattern fitting with fewer elements compared to conventional CS methods for the same aperture.

Conclusions:

• The CS-based approach effectively designs sparse vector hydrophone arrays.
• The method allows for simultaneous optimization of multiple channels, a significant advancement.
• The proposed algorithm offers improved efficiency and accuracy in underwater acoustic array design.