Unknown mutual coupling effect can degrade the performance of a direction of arrival (DOA) estimation method. In this letter, a new method is proposed for uniform linear arrays (ULAs) to tackle this problem. Considering the sparse representation exploiting the inherent structure of the received data, the effective block sparse representation and the convex optimization problem is constructed using the steering vector parameterizing method. The proposed solution based on the l1- SVD (singular value decomposition) can exploit the information provided by the whole array and the Toeplitz structure of the mutual coupling matrix (MCM) in the ULA. Simulation results are provided to demonstrate its performance with unknown mutual coupling in comparison with some existing methods.ud
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机译：未知的互耦效应会降低到达方向（DOA）估计方法的性能。在这封信中，提出了一种用于均匀线性阵列（ULA）的新方法来解决此问题。考虑到利用接收到的数据的固有结构的稀疏表示，使用转向矢量参数化方法构造了有效的块稀疏表示和凸优化问题。所提出的基于l1-SVD（奇异值分解）的解决方案可以利用ULA中整个阵列和互耦合矩阵（MCM）的Toeplitz结构所提供的信息。提供仿真结果，以证明与某些现有方法相比，其在未知互耦的情况下的性能。 ud
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