A Sparse Learning Approach to Multiple Noise-like Jammers Detection

In this paper, we focus on the problem of multiple noise-like jammer (NLJ) detection and develop an elegant and systematic framework for the design of an architecture that jointly detect an unknown number of NLJs and estimate the relevant parameters as the NLJ power, angle of arrival, and the number of threats. The followed approach relies on the likelihood ratio test in conjunction with a cyclic optimization procedure which incorporates at the design stage a sparsity promoting prior. As a matter of fact, the problem at hand owns an inherent sparse nature which is suitably exploited. Performance analysis is conducted on simulated data and shows the effectiveness of the proposed architectures in drawing a reliable picture of the electromagnetic threats illuminating the radar system.