基于协方差矩阵重构和导向矢量优化的波束形成算法

针对传统波束形成算法在导向矢量失配和协方差矩阵误差情况下输出信干噪比下降严重的问题,提出了一种基于协方差矩阵重构和导向矢量优化的稳健自适应波束形成算法。该算法通过估计信号和干扰的功率及方向,重构干扰加噪声协方差矩阵,同时结合投影和空域积分思想,对假定的导向矢量进行优化计算,使其接近真实的导向矢量。进而通过相关运算求得复数加权值实现波束形成。所提算法可以有效抑制干扰,提高输出信干噪比。在多种失配存在的情况下,所提算法也具有较好的性能。研究共进行了6个仿真实验,所提算法性能均优于所对比算法。所提算法在快拍数固定且存在导向矢量失配的情况下,相比于最差情况性能最优算法有约5 dB的输出信干噪比提升。在信噪比固定且存在导向矢量失配的情况下,相比于对比算法均有4 dB以上的性能提升。实验结果验证了所提算法的有效性。

The beamforming algorithm based on the combination of steering vector optimization and covariance matrix reconstruction

To solve the problem that the output signal-to-interference-plus-noise ratio (SINR) of the traditional beamforming algorithm decreases seriously under the condition of the mismatch of the steering vector and the error of the covariance matrix, a kind of robust adaptive beamforming algorithm based on the combination of steering vector optimization and covariance matrix reconstruction was proposed. By estimating the power and direction of signal and interference, interference plus noise covariance matrix was reconstructed. At the same time, combined with projection and spatial integral, the assumed steering vector was optimized to make it approximate to the actual steering vector. Then the complex weight was obtained by related calculation and the beamforming could be realized. The proposed algorithm can effectively suppress interference and improve the output SINR. For comparison, the performances of the proposed algorithm were simulated in six experiments. The simulation results show that the proposed algorithm has better performances. Compared with the worst-case performance optimization algorithm, the proposed algorithm has about 5 dB improvement in output SINR under the condition that the number of snapshots is fixed and the steering vector is mismatched. Compared with algorithms for comparison, in the case where the signal-to-noise ratio (SNR) is fixed and the steering vector is mismatched, the performance is improved by more than 4 dB. The simulation results verify the effectiveness of the proposed algorithm.