Knowledge-based recursive least squares techniques for heterogeneous clutter suppression

The design of knowledge-based adaptive algorithms has been dealt with for the cancellation of heterogeneous clutter. To this end, the application of the recursive least squares (RLS) technique has been revisited for the rejection of unwanted clutter, and modified RLS filtering procedures have been devised accounting for the spatial variation of the clutter power as well as of the disturbance covariance persymmetry property. Then the authors introduce the concept of knowledge-based RLS and explain how the a priori knowledge about the radar operating environment can be adopted for improving the system performance. Finally, the authors assess the benefits resulting from the use of knowledge-based processing both on simulated and on measured clutter data collected by the McMaster IPIX radar in November 1993.