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

Content Provider	IEEE Xplore Digital Library
Author	Fasoula, A. Driessen, H. van Genderen, P.
Copyright Year	2009
Description	Author affiliation: IRCTR, Delft University of Technology, The Netherlands (van Genderen, P.) \|\| Thales Nederland BV, Hengelo, The Netherlands (Driessen, H.) \|\| Thales Nederland BV, Delft, The Netherlands (Fasoula, A.)
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. A low-dimensional 2D spatial model, where information on the radar target signature is compressed, can be estimated using High Range Resolution (HRR) data from a sparse system of view angles. 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.
Starting Page	286
Ending Page	289
File Size	500801
Page Count	4
File Format	PDF
ISBN	9781424447473
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-30
Publisher Place	Italy
Access Restriction	Subscribed
Subject Keyword	Reflectivity Image coding Information resources Target recognition Surveillance Tomography Radar imaging Image sampling Sampling methods Spatial resolution
Content Type	Text
Resource Type	Article

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