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A robust, fast-converging, reduced-rank adaptive processor called the loaded reiterative median cascaded canceller (LRMCC) is introduced. The LRMCC exhibits the highly desirable combination of 1) convergence-robustness to outliers/targets/nonstationary data in adaptive weight training data, and 2) fast convergence at a rate commensurate with reduced-rank algorithms. Simulated jamming data as well as measured airborne radar data from the MCARM space-time adaptive processing (STAP) database are used to show performance enhancements. Performance is compared with the fast maximum likelihood (FML) and sample matrix inversion (SMI) algorithms. It is demonstrated that the LRMCC is easily implemented and is a highly robust replacement for existing reduced-rank adaptive processors, exhibiting superior performance in nonideal measured data environments.