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5 June 1995 Multiaperture SAR target detection using hidden Markov models
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Layne R. Flake, Ashok K. Krishnamurthy, and Stanley C. Ahalt "Multiaperture SAR target detection using hidden Markov models", Proc. SPIE 2487, Algorithms for Synthetic Aperture Radar Imagery II, (5 June 1995); https://doi.org/10.1117/12.210845
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