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In this paper, we address the problem of detecting multiple Noise-LikeJammers (NLJs) through a radar system equipped with an array of sensors. Tothis end, we develop an elegant and systematic framework wherein twoarchitectures are devised to jointly detect an unknown number of NLJs and toestimate their respective angles of arrival. The followed approach relies onthe likelihood ratio test in conjunction with a cyclic estimation procedurewhich incorporates at the design stage a sparsity promoting prior. As a matterof fact, the problem at hand owns an inherent sparse nature which is suitablyexploited. This methodological choice is dictated by the fact that, from amathematical point of view, classical maximum likelihood approach leads tointractable optimization problems (at least to the best of authors' knowledge)and, hence, a suboptimum approach represents a viable means to solve them.Performance analysis is conducted on simulated data and shows the effectivenessof the proposed architectures in drawing a reliable picture of theelectromagnetic threats illuminating the radar system.

A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers