A ML localizer of multiple radar targets

Abstract

In the present paper, we focus on the design and the analysis of schemes aimed at estimating the position of multiple point-like targets that fall among three adjacent samples of the matched filter output typically present in the processing chain of a radar. To this end, we exploit spillover of targets' energy to adjacent range cells (we model signals backscattered by the targets as coherent pulse trains). Moreover, useful signals are embedded in correlated Gaussian noise with unknown covariance matrix. Finally, for estimation purposes we assume that a set of secondary data, free of signal components, but sharing the same covariance matrix of the noise in the cells containing signal returns, is available. The analysis, also in comparison to two possible competitors, proves the superiority of multitarget schemes with respect to single target ones.