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Recursive Bayesian beamforming with uncertain projected steering vector and strong interferences

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Abstract

A recursive Bayesian beamforming is proposed for the steering vector uncertainty and strong interferences. Signal and noise powers are unknown, and beamforming weight is modeled as a complex Gaussian vector that characterizes the level of projected steering vector uncertainty. By applying the Bayesian model, a recursive algorithm is developed to estimate beamforming weight. Numerical simulations of linear and planar arrays demonstrate the effectiveness and robustness of the proposed beamforming algorithm. After convergence, the proposed algorithm exhibits a performance similar to that of the optimal \(\mathrm {MaxSINR}\) beamformer.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (Grant No. 11273017).

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Authors and Affiliations

  1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yubing Han & Vanha Tran

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  1. Yubing Han
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  2. Vanha Tran
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Correspondence to Yubing Han.

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Han, Y., Tran, V. Recursive Bayesian beamforming with uncertain projected steering vector and strong interferences. SIViP 10, 975–982 (2016). https://doi.org/10.1007/s11760-015-0848-3

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  • DOI: https://doi.org/10.1007/s11760-015-0848-3

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