Multiaspect target detection in SAR imagery using hidden Markov models

Paul R. Runkle; Lam H. Nguyen; Gary A. Ybarra; Lawrence Carin

doi:10.1117/12.357638

13 August 1999 Multiaspect target detection in SAR imagery using hidden Markov models

Paul R. Runkle, Lam H. Nguyen, Gary A. Ybarra, Lawrence Carin

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357638
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

It is well known that radar scattering from an illuminated object is often dependent on target-sensor orientation. In typical synthetic aperture radar (SAR) imagery, such aspect dependence is lost during image formation. We apply a sequence of directional filters to the SAR imagery to generate a sequence of images which recover the directional dependence over a corresponding sequence of subapertures. The scattering statistics associated with geometrically distinct target- sensor orientations are then used to design a hidden Markov model (HMM) for the target class. This approach explicitly incorporates the sensor motion into the model and accounts for the fact that the orientation of the target is assumed to be unknown. Performance is quantified by considering the detection of tactical targets concealed in foliage.

Citation Download Citation

Paul R. Runkle, Lam H. Nguyen, Gary A. Ybarra, and Lawrence Carin "Multiaspect target detection in SAR imagery using hidden Markov models", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357638

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