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Low-Complexity Iterative Adaptive Linearly Constrained Minimum Variance Beamformer

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Abstract

The closed-form solution of linearly constrained minimum variance (CF-LCMV) suffers heavy computational burden from two-matrix inversion when computing the optimal vector. CF-LCMV is not an adaptive beamformer and performs poorly with low signal-to-interference-plus-noise-ratio (SINR) and small number of snapshots. In this study, we derive a low-complexity iterative adaptive LCMV (IA-LCMV) algorithm based on conjugate gradient (CG) technique with threefold advantages: first, IA-LCMV can remarkably alleviate the complexity of CF-LCMV; second, IA-LCMV can adjust output adaptively with comparable convergence speed. Finally, it shows robust performance against low SINR and small number of snapshots. Simulation results demonstrate the efficacy of our proposed algorithms.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant (61201277), China Postdoctoral Science Foundation (No. 2012M510168), Postdoctoral Science Foundation of Jiangxi Province of China, and the Fundamental Research Funds for the Central Universities (No. ZYGX2010J018).

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Correspondence to Xiansheng Guo.

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Guo, X., Xu, B., Rao, Z. et al. Low-Complexity Iterative Adaptive Linearly Constrained Minimum Variance Beamformer. Circuits Syst Signal Process 33, 987–997 (2014). https://doi.org/10.1007/s00034-013-9668-2

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  • DOI: https://doi.org/10.1007/s00034-013-9668-2

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