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

Journal article (2010)

Authors

A Fasoula International Research Centre for Telecommunication and Radar
H Driessen External organisation
P. van Genderen International Research Centre for Telecommunication and Radar
Research Group
International Research Centre for Telecommunication and Radar
More Info
expand_more

Published Date

2010

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Research Group

International Research Centre for Telecommunication and Radar

Abstract

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.