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Reduced rank space-time adaptive processing with quadratic pattern constraints for airborne radar
Abstract
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.
© 2003 IEEE. The article (PDF) appeared in the Proceedings of the 37th Asilomar Conference on Signals, Systems, and Computers, pp. 807-811, November 2003. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.