协方差矩阵重构的稳健自适应波束形成算法
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摘要
针对协方差矩阵含有期望信号成分以及波束指向角失配时,传统自适应波束形成器性能严重下降的问题,提出了协方差矩阵重构的稳健自适应波束形成算法。该算法将全空域划分成若干互不重叠的区域,分别对应干扰区域与信号区域,先利用Capon波束形成器对干扰区域积分,由此构造出干扰协方差矩阵。然后,利用标准Capon波束形成器的波束域MUSIC谱估计法对信号区域积分,重构出信号协方差矩阵,以其主特征向量作为期望信号导引向量估计。由于算法重构了干扰加噪声协方差矩阵并对导引向量进行了修正,保证了自适应波束形成器的性能。理论分析和仿真实验结果表明,算法在训练数据含有期望信号成分和波束指向角度失配情况下具有良好的性能。
Considering that the sample covariance matrix contains desired signal and look direction mismatch in steering vector exist, the performance of the traditional adaptive beamformer degrads greatly, this paper proposes a robust adaptive beamforming algorithm based on covariance matrix reconstruction. It divides the whole spatial area into several non-overlapping regions, corresponding to the interference region and desired signal region, respectively. The standard Capon beamformer is used to estimate the interference power, then the improved interference covariance matrix can be constructed by integrating power inside the interference region. Secondly, the signal covariance matrix is reconstructed by the beamspace MUSIC spectral estimation method using the standard Capon beamformer which integrates the signal power inside the desired signal region, with its main eigenvector as the desired signal steering vector. Thus, it avoids the performance degradation of the adaptive beamforming caused by the desired signal and angle mismatch. Theory analysis and simulation have shown that algorithm has good performance in the case of training data contains desired signal and angle mismatch.
Considering that the sample covariance matrix contains desired signal and look direction mismatch in steering vector exist, the performance of the traditional adaptive beamformer degrads greatly, this paper proposes a robust adaptive beamforming algorithm based on covariance matrix reconstruction. It divides the whole spatial area into several non-overlapping regions, corresponding to the interference region and desired signal region, respectively. The standard Capon beamformer is used to estimate the interference power, then the improved interference covariance matrix can be constructed by integrating power inside the interference region. Secondly, the signal covariance matrix is reconstructed by the beamspace MUSIC spectral estimation method using the standard Capon beamformer which integrates the signal power inside the desired signal region, with its main eigenvector as the desired signal steering vector. Thus, it avoids the performance degradation of the adaptive beamforming caused by the desired signal and angle mismatch. Theory analysis and simulation have shown that algorithm has good performance in the case of training data contains desired signal and angle mismatch.
引文
1 Ehrenberg L, Gannot S, Leshem A et al. Sensitivity analysis of MVDR and MPDR beamformers. 2010 IEEE 26th Convention of Electrical and Electronics Engineers, Eilat,Israel, 2010:416-420
2 Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 1969; 57(8):1408—1418
3 Cox H, Zeskind R M, Owen M. Robust adaptive beamforming. IEEE Trans. Acoust. Speech Signal Process., 1987;35(10):1365-1376
4 Li J, Stoica P, Wang Z. On robust Capon beamforming and diagonal loading. IEEE Trans. Signal Process., 2003;51(7):1702-1715
5 Nai S E, Ser W, YU Z L et al. Iterative Robust Minimum Variance Beamforming. IEEE Trans. Signal Process., 2011; 59(4):1601-1611
6 Lie J P, Ser W, See C M S. Adaptive uncertainty based iterative robust capon beamformer using steering vector mismatch estimation. IEEE Trans. Signal Process., 2011;59(9):4483-4488
7 Zhang W, Wang J, And Wu S. Robust Capon beamforming against large DOA mismatch.Signal Processing,2013;93(4):804-810
8 Gu Y, Leshem A. Robust adaptive beamforming based on interference covariance matrix reconstruction and steeringvector estimation. IEEE Trans. Signal Process., 2012;60(7):3881-3885
9 Khabbazibasmenj A, Vorobyov S A, Hassanien A. Robust adaptive beamforming based on steering vector estimation with as little as possible prior information. IEEE Trans.Signal Process., 2012; 60(6):2974-2987
10 Shen F, Chen F, Song J. Robust adaptive beamforming based on steering vector estimation and covariance matrix reconstruction. IEEE Commun. Lett., 2015; 19(9):1636-1639
11 Kim S J, Magnani A, Mutapcic A,et al. Robust beamforming via worst-case SINR maximization. IEEE Trans.Signal Process., 2008; 56(4):1539-1547
12 Hassanien A, Vorobyov S A, Wong K M. Robust adaptive beamforming using sequential quadratic programming:An iterative solution to the mismatch problem. IEEE Signal Process. Lett., 2008; 15:733-736
13徐晓男,马启明,杜栓平.波束空间能量约束的稳健自适应波束形成.声学学报,2013; 38(3):258-261
14闫超,郭良浩,汪福全,葛凤翔.一种鲁棒性的最小方差无失真响应波束形成算法及其应用.声学学报,2011; 36(6):605-610
15 Trees H L. Optimum array processing-Part IV of detection, estimation, and modulation theory. New York:Wiley,2002:332-333
16 Huang L, Zhang J, Xu X, et al. Robust adaptive beamforming with a novel interference-plus-noise covariance matrix reconstruction method. IEEE Trans. Signal Process.,2015; 63(7):1643-1650
17 Bienvenu G, Kopp L. Decreasing high resolution method sensitivity by conventional beamformer preprocessing.IEEE International Conference on Acoustics, Speech, and Signal Processing, San Diego, California, 1984:714-717
18沈剑华.计算数学基础.同济大学出版社,1989
19 Somasundaram S D, Parsons N H. Evaluation of robust capon beamforming for passive sonar. IEEE J. Oceanic Eng., 2011; 36(4):686-695
20 Besson O, Stoica P. Decoupled estimation of DOA and angular spread for a spatially distributed source. IEEE Trans. Signal Process., 2000; 48(7):1872-1882
2 Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 1969; 57(8):1408—1418
3 Cox H, Zeskind R M, Owen M. Robust adaptive beamforming. IEEE Trans. Acoust. Speech Signal Process., 1987;35(10):1365-1376
4 Li J, Stoica P, Wang Z. On robust Capon beamforming and diagonal loading. IEEE Trans. Signal Process., 2003;51(7):1702-1715
5 Nai S E, Ser W, YU Z L et al. Iterative Robust Minimum Variance Beamforming. IEEE Trans. Signal Process., 2011; 59(4):1601-1611
6 Lie J P, Ser W, See C M S. Adaptive uncertainty based iterative robust capon beamformer using steering vector mismatch estimation. IEEE Trans. Signal Process., 2011;59(9):4483-4488
7 Zhang W, Wang J, And Wu S. Robust Capon beamforming against large DOA mismatch.Signal Processing,2013;93(4):804-810
8 Gu Y, Leshem A. Robust adaptive beamforming based on interference covariance matrix reconstruction and steeringvector estimation. IEEE Trans. Signal Process., 2012;60(7):3881-3885
9 Khabbazibasmenj A, Vorobyov S A, Hassanien A. Robust adaptive beamforming based on steering vector estimation with as little as possible prior information. IEEE Trans.Signal Process., 2012; 60(6):2974-2987
10 Shen F, Chen F, Song J. Robust adaptive beamforming based on steering vector estimation and covariance matrix reconstruction. IEEE Commun. Lett., 2015; 19(9):1636-1639
11 Kim S J, Magnani A, Mutapcic A,et al. Robust beamforming via worst-case SINR maximization. IEEE Trans.Signal Process., 2008; 56(4):1539-1547
12 Hassanien A, Vorobyov S A, Wong K M. Robust adaptive beamforming using sequential quadratic programming:An iterative solution to the mismatch problem. IEEE Signal Process. Lett., 2008; 15:733-736
13徐晓男,马启明,杜栓平.波束空间能量约束的稳健自适应波束形成.声学学报,2013; 38(3):258-261
14闫超,郭良浩,汪福全,葛凤翔.一种鲁棒性的最小方差无失真响应波束形成算法及其应用.声学学报,2011; 36(6):605-610
15 Trees H L. Optimum array processing-Part IV of detection, estimation, and modulation theory. New York:Wiley,2002:332-333
16 Huang L, Zhang J, Xu X, et al. Robust adaptive beamforming with a novel interference-plus-noise covariance matrix reconstruction method. IEEE Trans. Signal Process.,2015; 63(7):1643-1650
17 Bienvenu G, Kopp L. Decreasing high resolution method sensitivity by conventional beamformer preprocessing.IEEE International Conference on Acoustics, Speech, and Signal Processing, San Diego, California, 1984:714-717
18沈剑华.计算数学基础.同济大学出版社,1989
19 Somasundaram S D, Parsons N H. Evaluation of robust capon beamforming for passive sonar. IEEE J. Oceanic Eng., 2011; 36(4):686-695
20 Besson O, Stoica P. Decoupled estimation of DOA and angular spread for a spatially distributed source. IEEE Trans. Signal Process., 2000; 48(7):1872-1882