Leveraging Sparsity to Enhance Selectivity in Radar Tunable Architectures

In this paper, within the radar context, we explore the development of decision-making approaches capable of effectively rejecting mismatched signals within Gaussian interference with an unfamiliar covariance matrix. To achieve this, we leverage LASSO optimization as a sparse recovery framework, solved efficiently using the alternating direction method of multipliers (ADMM), to amplify the precision in estimating the target angle of arrival (AOA). The outcomes of this estimation procedure serve as the foundation for the detection frameworks, whether the onestep detector (OSD) or the two-step detector (TSD) approaches that rely on the generalized likelihood ratio test (GLRT) and adaptive matched filter (AMF). Importantly, these decisionmaking protocols provide a descent balance between detection performance (for matching signals) and rejecting undesired signals. During the analysis phase, we evaluate the effectiveness of our introduced detectors in comparison to the existing selective counterparts. The findings indicate that our proposed detectors surpass their counterparts in rejecting undesired signals, while sustaining a commendable level of detection performance for matching signals. Additionally, unlike most alternative methods, our proposed detectors demonstrate an acceptable level of execution time.