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

Sparse arrays reduce the number of sensors required to achieve a specific angular resolution by using sensor spacing greater than the half-wavelength. These undersampled sparse arrays require processing algorithms to eliminate aliasing ambiguities. Thinned arrays are sparse arrays whose sensor positions lie on an underlying equally spaced grid. Using data from a shallow water passive sonar experiment, this paper investigates two thinned array geometries (coprime and nested) along with two processing algorithms (multiplicative and min). Coprime and nested arrays consist of two interleaved Uniform Line Arrays (ULAs) where one or both of the ULAs are undersampled. Multiplicative and min processors combine the outputs of the conventionally-beamformed subarrays to estimate the spatial spectrum. While these nonlinear processors can suppress aliasing, they are often plagued by high sidelobes and cross term interference. This paper presents sparse array designs for a shallow waveguide that require 33% fewer sensors than a fully-sampled ULA and provide significant sidelobe attenuation. Experimental data analysis reveals that cross term interference dominates the spectral estimates for the coprime and nested multiplicative processors and the coprime min processor. The nested min processor outperforms its sparse counterparts due to its ability to contend with coherent multipath in the environment.