Spatial spectral estimation using a coprime sensor array with the min processor

Abstract

A coprime sensor array (CSA) estimates the spatial power spectral density (PSD) of the observed signal by multiplying one conventionally beamformed subarray scanned response with the complex conjugate of the other. This product processor removes the CSA subarray spatial aliasing ambiguities, but has a peak sidelobe higher than the peak sidelobe of a fully populated uniform linear array (ULA). Moreover, the resulting PSD estimate is not necessarily positive semi-definite. This paper proposes choosing the minimum of the two CSA subarray scanned responses at each bearing to resolve the spatial aliasing ambiguities. The min processor achieves lower peak sidelobe height and total sidelobe area than the product processor and preserves the positive semi-definite property of the PSD. This paper presents closed form expressions for the first two moments of the CSA min PSD estimator. Simulation results show the min processor achieves a lower PSD estimate variance than the product processor while maintaining the same array resolution.