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中文摘要：
为进一步提高神经网络模型的方向估计精度,提出利用特征矢量相角作为方向特征来构建模型。该方法首先通过协方差矩阵特征分解得到不易受噪声干扰的信号特征矢量;再对该矢量提取相角,信号的方向信息就包含在该相角中,以该相角作为输入矢量来训练模型。仿真结果证明了该方法具有抗噪能力强、模型估计精度高等特点,因此具有较高的工程应用价值。
英文摘要：
In order to get the higher precision in neural network-based DOA estimation,this paper proposed a novel approach using phase-angle of eigenvector as the direction character.This approach firstly decomposed the covariance matrix to get the phase-angle of signal eigenvector,which contained DOA information with less noise interference,so the phase-angles of eigenvector could be used as the input vectors to train RBFNN model.The simulation results show this method has more powerful anti-noise ability and higher estimation precision,etc.so it has a good engineering application value.
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