Abstract:
In this dissertation, we aim to improve the detection performances of radar systems. To this end, we propose and analyze two novel censoring techniques of undesirable samples, of a priori unknown positions, that may be present in the environment under investigation. Therefore, we consider heterogeneous backgrounds characterized by the presence of some irregularities such that clutter edge transitions and/or interfering targets. The first proposed processor,
termed automatic censoring constant false alarm (AC-CFAR), operates exclusively in a Gaussian background. It is built to allow the segmentation of the environment to regions and switch automatically to the appropriate detector;
namely, the cell averaging CFAR (CA-CFAR), the censored mean level CFAR (CMLD-CFAR) or the trimmed mean CFAR (TM-CFAR). Furthermore, the second proposed processor is termed generalized automatic censoring CFAR
(GAC-CFAR). Irrespective of the environment, being either Gaussian or compound Gaussian, this processor integrates an automatic censoring routing of interfering targets. This is essentially achieved through the use of a new
population drawn from the initial reference window. That is, in order to get a better estimation of the environment, this new population allows segregating the homogeneous set of reference cells from the undesirable one. Finally, to
evaluate the censoring performances of the proposed processors, a battery of Monte Carlo simulations, representing several real-world situations, has been conducted. This has helped to appreciate the real virtues of censoring of the
proposed processors. The obtained results show how convincing the approaches adopted in this research wok are and how large the openings they may suggest to future searchesare.
