Detecting Gaussian signals in the presence of interferers using the coprime sensor arrays with the min processor

Abstract

A coprime sensor array (CSA) requires fewer sensors to achieve similar spatial resolution as a fully populated ULA with the comparable aperture. CSAs commonly multiply the subarray beampatterns to resolve the ambiguities introduced by spatial aliasing. This paper proposes choosing the minimum between the CSA subarray spatial spectrum estimates for each bearing to resolve the aliasing ambiguities. The resulting CSA- min detection PDFs for each bearing are weighted sums of products of exponential functions and Marcum Q-functions. The CSA-min processor improves the ROC performances in detecting Gaussian signals in the presence of interferers and noise in Monte Carlo simulations.