阵列通道间的幅相误差导致导向矢量失配,会严重退化自适应波束形成的性能。现有稳健自适应波束形成(RAB)方法通过引入最差导向矢量失配误差约束或联合估计幅相误差和波束形成器权值矢量,以改善波束形成性能,但这些方法的计算复杂度高,且在有限快拍下性能有限。为此,文中在深度展开框架下提出一种基于交替方向乘子法(ADMM)的RAB 网络,以快速实现幅相误差和干扰协方差矩阵的联合估计。首先,建立存在阵列通道幅相误差时的干扰信号稀疏表示模型;然后,根据基于ADMM 的幅相误差和干扰稀疏表示系数联合估计算法,设计一种深度展开ADMM(DU-ADMM)网络,该网络的输入为接收到达的干扰信号,输出为幅相误差和干扰稀疏表示系数;最后,利用该网络的输出重构出干扰加噪声协方差矩阵,并生成稳健自适应波束形成器。仿真结果表明,DU-ADMM 网络可在单快拍场景下实现RAB,且能够以较少的网络层数更精确地估计出幅相误差,有效降低了计算量,同时可获得更高的输出信干噪比。

The gain-phase errors between different array channels lead to steering vector mismatch, which seriously degrades the performance of adaptive beamforming. The current robust adaptive beamforming (RAB) methods improve beamforming performance by introducing worst-case steering vector mismatch error constraints or jointly estimating gain-phase errors and beamformer weight vector. However, these methods consume high computational overhead and have poor performance under limited snapshot scenes. Owing to this, this article proposes an alternating direction multiplier method (ADMM)-based RAB network under the deep unfolding framework to quickly achieve joint estimation of gain-phase errors and interference covariance matrix. The sparse representation model of interference signals is first established in the presence of gain-phase errors. Then, according to the ADMM-based joint estimation method of gain-phase errors and sparse coefficients of interferences, a deep unfolding ADMM (DU-ADMM) network is proposed. Its input is the received interference signals and its output is gain-phase errors and interference sparse coefficients. Finally, the interference plus noise covariance matrix is recovered based on the network output and then used to generate a robust adaptive beamformer. Simulation results show that the DU-ADMM network can achieve RAB in a single snapshot scene. Moreover, it can estimate the gain-phase errors more accurately with fewer network layers, leading to reduced computational cost, and yield higher output signal-to-interference-plus-noise ratio.

TN957.51

国家自然科学基金资助目(62171224)