Reduced Rank Space-time Adaptive Processing with Quadratic Pattern Constraints for Airborne Radar
نویسندگان
چکیده
Reduced rank (RR) linearly constrained minimum variance (LCMV) adaptive beamforming with quadratic pattern constraints (QPC) is applied to space-time adaptive processing (STAP) for airborne radar. The problem is formulated for general rank reducing transformations and main beam and sidelobe pattern control is achieved by imposing a set of inequality constraints on the mean-square error between the adaptive pattern and a desired beampattern over a set of angle-Doppler regions. Both a fixed PRI-staggered post-Doppler transformation and a data-dependent principal component transformation are shown to perform at least as well as full-dimension LCMV-QPC STAP in terms of processing gain and sidelobe reduction with significantly reduced computational complexity.
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