A novel blind beamforming algorithm based on sparse Time-Frequency Decomposition (TFD) is proposed to solve the problems of existing blind beamforming algorithms: poor universality and the requirement of large amount of sampling data. In the proposed algorithm, the traditional Short-Time Fourier Transform (STFT) is first formulated as a sparse reconstruction problem. Then, a fast and efficient algorithm based on the alternating split Bregman technique is utilized to carry out the optimization. By combining the clustering and uncertainty set methods, the sparse-TFD results of the receiving data at each sensor are used to realize the estimation of Steering Vectors (SV). Finally, the optimal weight coefficients are achieved by substituting the estimated SV into the MVDR beamformer. The proposed algorithm hardly needs any specific statistical property of the receiving signals. Simulation results demonstrate that this algorithm can achieve superior output performance over the existing blind beamforming methods. It needs few snapshots with lower computational cost and has fast convergence rate, which makes the algorithm easy to utilize in practical applications.
HUANG Yan, LIAO Guisheng, LI Jun, et al. Non-ambiguity beamforming of nonuniform linear array based on consecutive difference coarray[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2891-2897. doi: 10.11999/ JEIT150321.
[4]
GU Yujie and LESHEM A. Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3881-3885. doi: 10.1109/TSP.2012. 2194289.
[5]
ZHANG Zhenyu, LIU Wei, LENG Wen, et al. Interference- plus-noise covariance matrix reconstruction via spatial power spectrum sampling for robust adaptive beamforming[J]. IEEE Signal Processing Letters, 2016, 23(1): 121-125. doi: 10.1109/LSP.2015.2504954.
[6]
COVIELLO C M and SIBUL L H. Blind source separation and beamforming: algebraic technique analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(1): 221-235. doi: 10.1109/TAES.2004.1292155.
[7]
XU Changjiang, FENG Guangzeng, and KYUNG K S. A modified constrained constant modulus approach to blind adaptive multiuser detection[J]. IEEE Transactions on Communications, 2001, 49(9): 1642-1648. doi: 10.1109/26. 950350.
[8]
CAI Yunlong, RODRIGO C D L, ZHAO Minjian, et al. Low- complexity variable forgetting factor mechanism for blind adaptive constrained constant modulus algorithms[J]. IEEE Transactions on Signal Processing, 2012, 60(8): 3988-4002. doi: 10.1109/TSP.2012.2199317.
[9]
SONG Xin, WANG Jinkuan, LI Qiuming, et al. Robust least squares constant modulus algorithm to signal steering vector mismatches[J]. Wireless Personal Communications, 2013, 68(1): 79-94. doi: 10.1007/s11277-011-0440-2.
[10]
WU Qiang and WONG K M. Blind adaptive beamforming for cyclostatioanary signals[J]. IEEE Transactions on Signal Processing, 1996, 44(11): 2757-2767. doi: 10.1109/78.542182.
ZHAO Liquan. Research on ICA algorithm and its application in array signal processing[D]. [Ph.D. dissertation], Harbin Engineering University, 2009: 1-18.
ZHAO Liquan, YANG Shenyuan, JIA Yanfei, et al. Fast fixed point blind beamforming algorithm for arbitrary non- gaussian signals[J]. Systems Engineering and Electronics, 2009, 31(7): 1577-1580. doi: 10.3321/j.issn:1001-506X.2009. 07.012.
[13]
JAFARI M G, WANG Wenwu, CHAMBERS J A, et al. Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals[J]. IEEE Transactions on Signal Processing, 2006, 54(3): 1028-1040. doi: 10.1109/TSP.2005.863005.
LIU Yaqi, LIU Chengcheng, ZHAO Yongjun, et al. A blind beamforming algorithm for multitarget signals based on time- frequency analysis[J]. Acta Physica Sinica, 2015, 64(11): 114302. doi: 10.7498/aps.64.114302.
LI Wenxing, MAO Xiaojun, and SUN Yaxiu. A new algorithm for null broadening beamforming[J]. Journal of Electronics & Information Technology, 2014, 36(12): 2882-2888. doi: 10.3724/SP.J.1146.2013.02018.
[16]
JOKANOVIC B and AMIN M. Reduced interference sparse time-frequency distributions for compressed observations[J]. IEEE Transactions on Signal Processing, 2015, 63(24): 6698-6709. doi: 10.1109/TSP.2015.2477056.
[17]
张贤达. 现代信号处理 [M]. 第2版, 北京: 清华大学出版社, 2002: 349-367.
ZHANG Xianda. Modern Signal Processing[M]. Second Edition, Beijing: Tsinghua University Press, 2002: 349-367.
[18]
GHOLAMI A. Sparse time-frequency decomposition and some applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3598-3604. doi: 10.1109/TGRS. 2012.2220144.
[19]
SCHNASS K and VANDERGHEYNST P. Dictionary preconditioning for greedy algorithms[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1994-2002. doi: 10.1109/ TSP.2007.911494.
[20]
CHEN Liwen, ZHENG Jiansheng, SU Mingkun, et al. A novel beamforming technique: Introducing a convex constrained optimization and compressed-sensing model[J]. IEEE Antennas and Propagation Magazine, 2016, 58(4): 48-59. doi: 10.1109/MAP.2016.2569476.
[21]
ZOU Jian, FU Yuli, ZHANG Qiheng, et al. Split Bregman algorithms for multiple measurement vector problem[J]. Multidimensional Systems and Signal Processing, 2015, 26(1): 207-224. doi: 10.1007/s11045-013-0251-6.
[22]
HUANG Lei, ZHANG Jing, XU Xu, et al. Robust adaptive beamforming with a novel interference-plus-noise covariance matrix reconstruction method[J]. IEEE Transactions on Signal Processing, 2015, 63(7): 1643-1650. doi: 10.1109/TSP. 2015.2396002.
[23]
LI Jian, STOICA P, and WANG Zhisong. On robust capon beamforming and diagonal loading[J]. IEEE Transactions on Signal Processing, 2003, 51(7): 1702-1714. doi: 10.1109/TSP. 2003.812831.
[24]
LUO Yuhui, WANG Wenwu, CHAMBERS J A, et al. Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques [J]. IEEE Transactions on Signal Processing, 2006, 54(6): 2198-2212. doi: 10.1109/TSP.2006.873367.