互耦效应下一种基于实值稀疏表示的波达方向估计算法

针对未知互耦条件下的波达方向（DOA）估计问题，提出了一种未知互耦条件下基于实值稀疏表示的加权子空间DOA估计算法。新算法利用一个特定的酉变换矩阵，将一个复杂的复值优化问题转化为一个实值优化问题，从而有效地将原问题的计算复杂度减少4倍以上。此外，为了进一步提高稀疏表示的估计算法估计精度，在原有l1 范数优化模型基础上引入一个能使得DOA估计方差取得最小值的最优子空间加权矩阵。仿真实验表明，在低信噪比情况下，新算法能进一步提高稀疏表示的估计算法抗噪能力，获得更好的估计精度。

A Real-valued Sparse Representation Method for DOA Estimation with Unknown Mutual Coupling

The paper presents a real-valued sparse representation method for DOA estimation in the presence of unknown mutual coupling. Utilizing a certain unitary transformation and taking advantage of the special structure of mutual coupling matrix (MCM) for uniform linear arrays (ULAs), we are able to convert complex-valued manifold matrices of ULAs with unknown mutual coupling into real ones. Due to this transformation, the computational complexity can be decreased by a factor of at least four. Moreover, the proposed method is expected to have a better noise suppression, as it exploits an additional optimal weighting matrix. Thus, the proposed method outperforms the original one, especially when signal-to-noise ratio (SNR) is low. Simulation results verify the efficiency of the proposed method.