Linear prediction of range-dependent inverse covariance matrix

In this paper, we study issues associated with applying linear prediction theory to the range-dependent inverse covariance matrix (PICM) in bistatic airborne radar systems. For bistatic ground moving target indication radar, the clutter Doppler frequency depends on range for all array geometries. This range dependency leads to problems in clutter suppression through STAP techniques. An analysis of a new technique, proposed to obtain an estimate of the inverse covariance matrix using least squares estimation, is provided in this paper. Simulation results show a significant improvement in processor performance, as compared to conventional STAP methods.