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中文摘要：
理想条件下，均匀线阵的互耦矩阵可用一带状、对称Toeplitz矩阵进行建模。然而实测数据表明，均匀线阵的互耦矩阵具有对称性，但不具有Toeplitz性，此时仍按理想情况建模，会导致DOA估计不准甚至完全失效。基于RBF神经网络，提出了互耦矩阵非Toeplitz条件下的DOA估计方法。算法利用了信号协方差矩阵的对称性和对角线元素不含信号DOA信息的特点，取协方差矩阵的上三角的元素作为网络输入，不仅减少了网络的输入数，同时还提高了与阵列法线夹角60°外的DOA估计精度。实验仿真结果验证了算法的有效性。
英文摘要：
A banded symmetric Toeplitz matrix provides a satisfactory model for the mutual coupling of ULA（Uniform Linear Array） under the ideal condition. But in fact, it is known from the practical data that the mutual coupling of ULA is symmetric but not Toeplitz. If mutual coupling matrix（MCM） is still modeled under the ideal condition, the estimated values of DOA will he inaccurate and even invalid. Based on RBF neural network, a kind of DOA estimation algorithm under non Toeplitz MCM is proposed in this paper. The characteristics of symmetry property and no DOA information on the diagonal of the correlation matrix are utilized to extract the upper triangular half of the matrix as the input vectors. This method not only reduces the dimension of the input vectors but also improves the DOA estimation precision out of 60 degree of the bevel between the signal and normal line of the array. Simulation results demonstrate that the proposed algorithm is efficient and valid.
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Decoupled 2D direction of arrival estimation using compact uniform circular arrays in the presence o
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