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Robust blind adaptive beamforming under double constraints

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Abstract

In practical complex communication environments, the performance of linearly constrained constant modulus algorithm (CMA) is known to degrade severely in the presence of even slight signal steering vector mismatches. To overcome the mismatches, a novel robust CMA is proposed for blind adaptive beamforming via the worst-case performance optimization and the oblique projection of signal steering vector, which is based on double constraints on explicit modeling of uncertainties in the desired signal array in this paper. To improve robustness, the weight vector is optimized to involve minimization of a constant modulus algorithm objective function with penalty for the worst-case signal steering vector. The theoretical analysis for our proposed algorithm in terms of SINR and convergence performance is presented in this paper. The proposed robust constrained CMA resolves the problem of interference capture, provides excellent robustness against the signal steering vector mismatches, and improves array output performance. Simulation results are presented to show the excellence of this technique and the main parameters of concern to evaluate the performance are analyzed.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their insightful comments that helped improve the quality of this paper. This work was supported by the National Natural Science Foundation of China under Grant no. 60874108 and no. 61004052, and by the Fundamental Research Funds for the Central Universities under Grant no. N090323002.

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Correspondence to Xin Song.

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Song, X., Wang, J. & Wang, B. Robust blind adaptive beamforming under double constraints. Neural Comput & Applic 22, 295–302 (2013). https://doi.org/10.1007/s00521-011-0684-5

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  • DOI: https://doi.org/10.1007/s00521-011-0684-5

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