De-ghosting of tomographic images in a radar network with sparse angular sampling

Taking into account sparsity of the reflectivity function of several radar targets of interest, efficient low-complexity automatic target recognition (ATR) systems can be designed. Such ATR systems would be based on two-dimensional (2D) spatial target models of low dimensionality, where critical information on the radar target signature is summarized. Discrete 2D radar target models can be estimated using high range resolution (HRR) data, measured at a sparse system of view angles. This being the main objective, incoherent tomographic processing of HRR data from a distributed surveillance system, made up of several radar nodes, is studied in this paper. A sparse angular sampling scheme is proposed, which exploits diversity due to both the distributed radar system and the target motion. The novelty is in the exploitation of this locally dense, but otherwise sparse set of viewing angles of the targets, obtained using a sparse network of radars. The de-ghosting efficiency of such a sampling scheme is demonstrated geometrically. This results in identification of minimal information resources for unambiguous estimation of a 2D target model, useful for radar target classification.