NDLI: Transformation-based linearly constrained reduced rank adaptive beamforming

Content Provider	IEEE Xplore Digital Library
Author	Ding Qian-Jun Wang Yong-Liang Zhang Yong-Shun Li Rong-Feng
Copyright Year	2005
Description	Author affiliation: Air Force Eng. Univ., Xi'an, China (Ding Qian-Jun)
Abstract	Linear constraints, such as null constraints or derivative constraints, can be obtained by linearly constrained minimum variance beamformer (LCMVB) in adaptive beamforming. With a limited sample support, reduced rank processing is applied to LCMVB to reduce the adaptive degrees of freedom, improve the convergence performance, and reduce the computational complexity. In application of the adaptive radar, the desired signal is not involved in the training data used to compute the adaptive weight vector. In this paper, the T-LCMVB architecture is applied in reduced rank processing of the adaptive radar. The transformation-based versions of linearly constrained eigencanceler, and linearly orthogonal projection algorithm are proposed, named as T-LCEC, and T-LCOP respectively. T-LCEC has an excellent numerical stability even if the interference in the training data falls into the constrained nulls. T-LCOP can preserve null constraints wherever the interferences are spaced relative to the constrained nulls.
Sponsorship	Nat. Capital Area Council IEEE Aerospace and Electron. Syst. Soc. RADAR Syst. Panel
Starting Page	647
Ending Page	652
File Size	297431
Page Count	6
File Format	PDF
ISBN	078038881X
DOI	10.1109/RADAR.2005.1435906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Array signal processing Interference constraints Radar Computational complexity Training data Numerical stability Vectors Filtering Adaptive filters Computer architecture
Content Type	Text
Resource Type	Article

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