Search

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 SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.