Robust adaptive signal processing methods for heterogeneous radar clutter scenarios

This paper addresses the problem of radar target detection in severely heterogeneous clutter environments. Specifically we present the performance of the normalized matched filter (NMF) test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the NMF test retains invariance with respect to the unknown scaling as well as the background noise level and is approximately CFAR. Performance of the test depends only upon the number of elements, number of pulses processed in a coherent processing interval and the rank of the clutter covariance matrix. Analytical expressions for calculating the false alarm and detection probabilities are presented. Performance of the method is shown to degrade with increasing clutter rank especially for low false alarm rates. An adaptive version of the test is developed and its performance is studied with simulated data. A technique known as self censoring reiterative fast maximum likelihood/adaptive power residue (SCRFML/APR) is presented to overcome the problem of outliers in training data for heterogeneous clutter scenarios.