Contents I. Introduction
Direction of arrival (DOA) estimation of an incoming signal is one of key-operation in modern receiving systems. As a matter of fact, several methodologies have been developed during the years to properly solve this important issue that spans a wide family of applications in the context of signal processing, viz. from radar to sonar, to wireless communications, etc. This so wide interest has driven many researches in developing algorithms capable of accurately estimating the DOA of the signal impinging on the receiving antenna. Among them, of paramount importance are the classic Capon [1], ESPRIT [2], MUSIC, as well as the plethora of works extending them and that, in general, exploits several kinds of advanced statistical and signal processing techniques [3]–[11]. Additionally, several procedures for DOA estimation exploiting the sparse nature of the signal model associated with targets/signals in number less than possible sources angle of arrivals (AOAs) in the region of interest have been devised [12]–[16]. Within this latter category, the sparse learning via iterative minimization (SLIM) developed in [12] consists in a regularized minimization algorithm that also applies an -norm constraint to obtain an accurate estimate of the signal angle, range, and Doppler. Moreover, SLIM is characterized by a capability of offering satisfactorily estimation performance with a relatively low computational burden.
