Maximum-likelihood based range-dependence compensation for coherent multistatic STAP radar

We study the performance of space-time adaptive processing (STAP) applied to coherent multistatic radar. The radars we consider are passive (receive only) but are synchronized in order to allow coherent processing of the data among the different radars. One of the major benefit of multistatism is that the full velocity vectors of the targets can be measured. Furthermore, we study the feasibility of estimating the clutter covariance matrix, required to compute the STAP filter. We show that range-dependence effects cause the usual sample matrix inversion (SMI) scheme to fail and we propose a method able to cope with arbitrary multistatic geometries.