Reduced rank space-time adaptive processing with quadratic pattern constraints for airborne radar

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.