白噪声增益约束的最差情况性能最优自适应波束形成方法
Adaptive beamforming using worst-case performance optimization with white-noise gain constraint
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摘要: 最差情况性能最优鲁棒自适应波束形成方法的性能十分依赖协方差矩阵和导向向量不确定集参数的选取, 在低信噪比或大误差条件下衰减较为严重, 这种现象等价于阵列接收到一定量的非相关白噪声引起的性能衰退。针对该问题, 改变最差情况性能最优方法中协方差矩阵不确定集参数在所有扫描角度采用统一取值的方式, 引入白噪声增益约束, 在不同角度上独立地优化协方差矩阵不确定集参数的取值, 提出一种拓展最差情况性能最优鲁棒自适应波束形成的方法, 并给出了白噪声增益约束参数的合理取值策略。数值仿真和海试数据处理结果均表明, 该方法在强干扰条件下具有良好的弱目标信号检测能力, 目标功率估计较为准确, 对导向向量误差具有强鲁棒性, 并且算法性能对取值策略所确定的白噪声增益约束参数值的敏感度较低。Abstract: The performance of a robust adaptive beamformer using worst-case performance optimization (WCPO) depends heavily on the selection of the covariance matrix and steering vector uncertainty set parameters, and deteriorates significantly under low signal-to-noise ratio or large error conditions, which is equivalent to the performance degradation caused by the array receiving a certain amount of uncorrelated white noise. To address this issue, the original WCPO of using uniform values for covariance matrix uncertainty set parameters across all scanning angles is modified. By introducing a white noise gain constraint, the covariance matrix uncertainty set parameters are optimized independently for different angles, leading to a new extension of the WCPO robust adaptive beamformer. Furthermore, a reasonable strategy for selecting the parameters of the white noise gain constraint is provided. The results of simulated and experimental data both show that the new approach has a good performance in detecting the weak signal against strong interferences, provides accurate target power estimation, and shows strong robustness to steering vector errors. In addition, the algorithm performance is less sensitive to the parameters of the white noise gain constraint determined by the proposed strategy.