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Proceedings Paper

Multiaperture SAR target detection using hidden Markov models
Author(s): Layne R. Flake; Ashok K. Krishnamurthy; Stanley C. Ahalt
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Paper Abstract

Multi-aperture SAR (MASAR) is an extension of conventional SAR imagery that allows anisotropic returns from man-made objects to be exploited for detection. In this paper, we propose a MASAR ATD algorithm based on hidden Markov models (HMMs). We show that this algorithm economically locates anisotropic target returns. Using simulated L-band MASAR imagery containing M35 trucks, generated by the Xpatch-es software written by Loral, we derive HMM structures that efficiently model the sub-aperture radar return trajectories for target, grass, and tree pixels. We compare HMM detection results for the simulated MASAR imagery over a 105 degree angle of integration with two alternative methods: MASAR split-aperture (SA) change detection and conventional SAR two-parameter CFAR detection. To obtain our results we group detected pixels into target-sized clusters using a clustering algorithm. The results show that HMM detection far outperforms CFAR detection while requiring 1/10th as many FLOPS per pixel. Further, HMM ATD is nearly as accurate as SA change detection while requiring less than 1/500th as many FLOPS per pixel. Finally, for a more practical 45 degree angle of integration, we show that HMM detection and SA chnage detection have equivalent performance, while HMM detection requires less than 1/185th as many FLOPS per pixel.

Paper Details

Date Published: 5 June 1995
PDF: 12 pages
Proc. SPIE 2487, Algorithms for Synthetic Aperture Radar Imagery II, (5 June 1995); doi: 10.1117/12.210845
Show Author Affiliations
Layne R. Flake, The Ohio State Univ. (United States)
Ashok K. Krishnamurthy, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 2487:
Algorithms for Synthetic Aperture Radar Imagery II
Dominick A. Giglio, Editor(s)

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