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

Radar scattering from an illuminated object is often dependent on target-sensor orientation. In synthetic aperture 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 matching 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 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.