Median cascaded canceller using reiterative processing

Abstract

A novel, robust adaptive processor is introduced, based on reiterative application of the median cascaded canceller (MCC). The MCC, though a highly robust adaptive processor, has a convergence rate that generally is dependent on the effective rank of the interference-plus-noise covariance matrix. The reiterative median cascaded canceller (RMCC) introduced here exhibits the highly desirable combination of 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance independent of the interference-plus-noise covariance matrix and at a rate commensurate with the sample matrix inversion (SMI) algorithm, unlike the MCC. Both simulated data as well as measured airborne radar data from the MCARM space-time adaptive processing (STAP) database are used to show performance enhancements. It is concluded that the RMCC adaptive processor is a highly robust replacement for the SMI adaptive processor and all its equivalent implementations.