A robust loaded reiterative median cascaded canceller
Michael L. Picciolo - SAIC, Karl Gerlach - NRL
Wed, 28 April 2004, 8:00 AM - 9:30 AM
A robust, fast-converging, reduced-rank adaptive processor is introduced, based on diagonally loading the Reiterative Median Cascaded Canceller (RMCC). The new Loaded Reiterative Median Cascaded Canceller (LRMCC) exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets/non stationary data in adaptive weight training data, like the RMCC, 2) convergence performance that is approximately independent of the interference-plus-noise covariance matrix, like the RMCC, and 3) fast convergence at a rate commensurate with reduced-rank algorithms, unlike the RMCC. Measured airborne radar data from the MCARM Space-Time Adaptive Processing (STAP) database is used to show performance enhancements. It is concluded that the LRMCC is a practical and highly robust replacement for existing reduced-rank adaptive processors, exhibiting superior performance in non ideal measured data environments.
Dr. Michael L. Picciolo - SAIC
Dr. Michael Picciolo is an Adaptive Signal Processing Analyst at SAIC in Chantilly, VA. He works in the areas of robust adaptive signal processing algorithm development, Space-Time Adaptive Processing (STAP), SAR and GMTI radar, and Image Processing. He received his Ph.D. in Electrical Engineering from Virginia Tech in 2003, an MSEE from Catholic University in 1993 and a BSEE from Clarkson University in 1988. Previously he spent 14 years at the Radar Division of the Naval Research Laboratory (NRL). At NRL he participated in advanced radar system designs for Navy shipboard adaptive radars and Navy airborne adaptive radars including future STAP systems. His research has yielded novel, full and reduced rank, robust adaptive signal processing algorithms for general applications with emphasis on STAP applications.