Multi-aspect target detection for SAR imagery using hidden Markov models

Radar scattering from an illuminated object is often highly dependent on th e target-sensor orientation. In typical synthetic aperture radar (SAR) imag ery, the information in the muiti-aspect target signatures is diffused in t he image-formation process. In an effort to exploit the aspect dependence o f the target signature, we employ a sequence of directional filters to the SAR imagery, thereby generating a sequence of subaperture images that recov er the directional dependence of the target scattering. The scattering stat istics are then used to design a hidden Markov model (HMM), wherein the ori entation-dependent scattering statistics are exploited explicitly This appr oach fuses information embodied in the orientation-dependent target signatu re under the assumption that both the target identity and orientation are u nknown. Performance is assessed by considering the detection of tactical ta rgets concealed in foliage, using measured foliage-penetrating (FOPEN) SAR data.