Multiplicative and min processing of experimental passive sonar data from thinned arrays.

Area of Science:

• Acoustics
• Signal Processing
• Array Signal Processing

Background:

• Sparse arrays offer reduced sensor counts for enhanced angular resolution.
• Undersampled sparse arrays necessitate advanced algorithms to resolve aliasing ambiguities.
• Thinned arrays, a subset of sparse arrays, utilize sensor positions on an underlying equally spaced grid.

Purpose of the Study:

• Investigate coprime and nested thinned array geometries in shallow water passive sonar.
• Evaluate multiplicative and min processing algorithms for aliasing suppression.
• Present sparse array designs offering reduced sensor count and improved sidelobe attenuation.

Main Methods:

• Utilized data from a shallow water passive sonar experiment.
• Implemented coprime and nested array configurations using interleaved Uniform Line Arrays (ULAs).
• Applied multiplicative and min processors to combine subarray outputs for spatial spectrum estimation.

Main Results:

• Sparse array designs achieved 33% fewer sensors than a fully-sampled Uniform Line Array (ULA).
• Cross-term interference significantly impacted spectral estimates for coprime/nested multiplicative and coprime min processors.
• The nested min processor demonstrated superior performance, effectively managing coherent multipath.

Conclusions:

• Sparse arrays, particularly the nested min processor, offer efficient solutions for passive sonar in shallow waveguides.
• The nested min processor's ability to handle multipath makes it advantageous over other sparse array techniques.
• Careful selection of array geometry and processing algorithm is crucial for mitigating interference in sparse array systems.