基于稀疏时频分解的盲波束形成算法

Blind Beamforming Algorithm Based on Sparse Time-frequency Decomposition

针对现有盲波束形成算法通用性差,所需采样数据量大等问题,该文提出一种基于稀疏时频分解的盲波束形成算法。算法首先将传统的短时傅里叶变换转化为稀疏重构问题,利用交替分裂Bregman算法进行迭代求解。然后利用对各阵元的接收信号进行稀疏时频分解的结果,结合聚类和不确定集方法,实现导向矢量的最优估计。最后利用MVDR算法获得最优权矢量。该算法无需利用信号统计特性,实现了高效的盲波束形成。仿真实验结果表明,该算法所需数据量小,迭代步骤易于工程实现,较现有盲波束形成算法输出性能更优,适用范围更广。

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.