Linear prediction of range-dependent inverse covariance matrix (PICM) sequences

Knowledge of the true clutter covariance matrix is required for optimum space–time adaptive processing (STAP). In practise, this matrix is not known and must be estimated from training data. 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. In this paper, we propose a new training strategy for STAP by using linear prediction techniques (least squares estimation) to obtain an estimate of the inverse covariance matrix. We present the issues associated with applying linear prediction theory to the range-dependent inverse covariance matrix in bistatic airborne radar systems. Performance measures are compared against conventional STAP techniques in terms of improvement factor loss plots.