The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed. In this method, the angular space is discretized, defining many non-overlapping small grids which cover the desired large angular space. First, a BCS estimation is run in each of the sectors to estimate the DoA of the signals. Then, a second stage is performed to correct the inconsistencies at the edges due to signal leaking between sectors. The performance of the method has been analyzed via extensive Monte-Carlo simulations in which the number of targets, their Radar Cross Section (RCS), and their location have been varied in a large extent, and the targets were observed by a Frequency Modulated Continuous Wave (FMCW) radar with an 86-element Uniform Linear Array (ULA). The results are compared with state-of-the-art methods in terms of estimation accuracy and resolution. Moreover, an analysis of the computational time, critical for many real-time applications, is presented, which shows a reduction of 20 times in the computational time compared with the standard BCS. Finally, the method has also been validated using experimental data collected with a commercial automotive radar.@en

In this paper, an algorithm to generate a sparse linear antenna array for Direction of Arrival (DoA) estimation that works well in combination with Bayesian Compressive Sensing (BCS) is proposed. The proposed algorithms rely on the provided information inherent to BCS, i.e., the entropy of the recovered estimation vector, to place new sensor antenna elements in an initially empty array, so that the most additional information is gathered about the observed scene. It is shown by means of simulation and radar measurements that BCS methods for DoA estimation using sparse sensor arrays provide promising results in terms of detection probability and estimation accuracy. Furthermore, the proposed algorithms are able to generate sparse sensor arrangements which provide an improved performance when compared against randomly generated sparse arrays.@en