Abstract
We propose a low complexity robust beamforming method for the general-rank signal model, to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices, but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion, a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.
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References
Capon, J., 1969. High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE, 57(8):1408–1418. http://dx.doi.org/10.1109/PROC.1969.7278
Chen, H., Gershman, A.B., 2008. Robust adaptive beamforming for general-rank signal models with positive semi-definite constraint. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.2341–2344. http://dx.doi.org/10.1109/ICASSP.2008.4518116
Chen, H., Gershman, A.B., 2011. Worst-case based robust adaptive beamforming for general-rank signal models using positive semi-definite covariance constraint. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.2628–2631. http://dx.doi.org/10.1109/ICASSP.2011.5947024
Cirrincione, G., Cirrincione, M., Hérault, J., et al., 2002. The MCA EXIN neuron for the minor component analysis. IEEE Trans. Neur. Netw., 13(1):160–187. http://dx.doi.org/10.1109/72.977295
Cox, H., Zeskind, R., Owen, M., 1987. Robust adaptive beamforming. IEEE Trans. Acoust. Speech Signal Process., 35(10):1365–1376. http://dx.doi.org/10.1109/TASSP.1987.1165054
de Maio, A., de Nicola, S., Farina, A., et al., 2010. Adaptive detection of a signal with angle uncertainty. IET Radar Sonar Navig., 4(4):537–547. http://dx.doi.org/10.1049/iet-rsn.2009.0106
Grant, M., Boyd, S., Ye, Y.Y., 2015. CVX: MATLAB Software for Disciplined Convex Programming. Available from http://cvxr.com/cvx/.
Gu, Y., Leshem, A., 2012. Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation. IEEE Trans. Signal Process., 60(7):3881–3885. http://dx.doi.org/10.1109/TSP.2012.2194289
Hassanien, A., Vorobyov, S.A., Wong, K.M., 2008. Robust adaptive beamforming using sequential programming: an iterative solution to the mismatch problem. IEEE Signal Process. Lett., 15:733–736. http://dx.doi.org/10.1109/LSP.2008.2001115
Khabbazibasmenj, A., Vorobyov, S.A., 2013. Robust adaptive beamforming for general-rank signal model with positive semi-definite constraint via POTDC. IEEE Trans. Signal Process., 61(23):6103–6117. http://dx.doi.org/10.1109/TSP.2013.2281301
Li, J., Stoica, P., Wang, Z.S., 2003. On robust capon beamforming and diagonal loading. IEEE Trans. Signal Process., 51(7):1702–1715. http://dx.doi.org/10.1109/TSP.2003.812831
Shahbazpanahi, S., Gershman, A.B., Luo, Z.Q., et al., 2003. Robust adaptive beamforming for general-rank signal models. IEEE Trans. Signal Process., 51(9):2257–2269. http://dx.doi.org/10.1109/TSP.2003.815395
Trump, T., Ottersten, B., 1996. Estimation of nominal direction of arrival and angular spread using an array of sensors. Signal Process., 50(1–2):57–69. http://dx.doi.org/10.1016/0165-1684(96)00003-5
van Trees, H.L., 2002. Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory. John Wiley & Sons, New York, USA.
Vorobyov, S.A., 2012. Principles of minimum variance robust adaptive beamforming design. Signal Process., 93(12):3264–3277. http://dx.doi.org/10.1016/j.sigpro.2012.10.021
Vorobyov, S.A., Gershman, A.B., Luo, Z.Q., 2003. Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem. IEEE Trans. Signal Process., 51(2):313–324. http://dx.doi.org/10.1109/TSP.2002.806865
Zhang, L., Liu, W., 2012a. Robust forward backward based beamformer for a general-rank signal model with realvalued implementation. Signal Process., 92(1):163–169. http://dx.doi.org/10.1016/j.sigpro.2011.07.001
Zhang, L., Liu, W., 2012b. Robust beamforming for coherent signals based on the spatial-smoothing technique. Signal Process., 92(11):2747–2758. http://dx.doi.org/10.1016/j.sigpro.2012.05.008
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Project supported by the Fundamental Research Funds for the Central Universities, China (No. K5051202047)
ORCID: Yu-tang ZHU, http://orcid.org/0000-0002-6826-4307
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Zhu, Yt., Zhao, Yb., Liu, J. et al. Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint. Frontiers Inf Technol Electronic Eng 17, 1245–1252 (2016). https://doi.org/10.1631/FITEE.1601112
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DOI: https://doi.org/10.1631/FITEE.1601112