Response Vector Constrained Robust LCMV Beamforming Based on Semidefinite Programming

Although linearly constrained minimum variance (LCMV) beamforming is robust against imprecise target information, it usually leads to relatively high sidelobe and distorted mainlobe which would induce a high false alarm probability. To circumvent this problem, this work devises a novel robust LCMV b...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 63; no. 21; pp. 5720 - 5732
Main Authors: Xu, Jingwei, Liao, Guisheng, Zhu, Shengqi, Huang, Lei
Format: Journal Article
Language:English
Published: IEEE 01.11.2015
Subjects:
Array signal processing
Arrays
Clutter
Degradation
linearly constrained minimum variance beamformer
quadratically constrained quadratic programming
Radar
response vector optimization
Robust adaptive beamforming
Robustness
semidefinite programming
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Summary:Although linearly constrained minimum variance (LCMV) beamforming is robust against imprecise target information, it usually leads to relatively high sidelobe and distorted mainlobe which would induce a high false alarm probability. To circumvent this problem, this work devises a novel robust LCMV beamforming approach by utilizing response vector optimization. It intends to find the optimal response vector in lieu of the all-one response vector in traditional LCMV beamformer. The proposed robust beamformer is first formulated as a nonconvex quadratically constrained quadratic programming problem, and then transformed into a semidefinite programming problem which can be efficiently and exactly solved. The proposed beamformer not only improves the performance in terms of signal-to-interference-plus-noise ratio substantially, but also possesses low sidelobe and well-maintained mainlobe. Moreover, since the response vector is quite small in size, the complexity of calculating the optimal response vector is negligible. Additionally, the proposed beamformer is also extended to two-dimensional space-time adaptive processing. Simulation results are presented to demonstrate the superiority of the proposed approach.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2015.2460221