Multi-aspect target detection for SAR imagery using hidden Markov models and two-dimensional matching pursuits

摘要

Radar scattering from an illuminated object is often dependent on target-sensor orientation. In synthetic apperure radar (SAR) imagery, the aspect dependence of the target over the aperture is lost during image formation. To recover this directional dependence, we post-processes the SAR imagery to generate a sequence of images over a corresponding sequence of subapertures. Features are extracted from the sequence of subaperture images using a two-dimensional m atching pursuits algorithm. The feature 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 a ccounts 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.