Direction-of-Arrival Estimation Using Eigenvectors of Covariance Matrix of Circular Array
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摘要: 为解决传统高分辨方位估计(DOA)算法计算量大、不稳健的问题, 文章首先利用圆环阵空间均匀噪声场中噪声协方差矩阵的特征向量重新定义了不同阶数的特征向量和阵列流形向量, 并将数据采样协方差矩阵根据阶数的大小进行了降维处理, 最后利用新的阵列流形向量和降维的数据采样协方差矩阵采用最小方差无失真响应(MVDR)进行目标方位估计。仿真结果表明, 在没有误差的情况下, 所提方法的最高阶方位估计结果与传统MVDR一致; 存在幅度和相位误差时, 更稳健的低阶方位估计的结果要优于传统MVDR方法, 在提升了抗误差稳健性的同时, 降维的数据协方差矩阵也大大减少了求逆的计算量。海试结果验证了文中方法的有效性, 采用的12元均匀圆环阵, 其2阶和3阶方位估计的结果要优于传统的MVDR方法。文中方法可为水下无人系统等平台上的圆环阵水下目标方位估计提供应用参考。Abstract: To solve the problem that the traditional high-resolution direction-of-arrival(DOA) estimation algorithms have a large amount of computation and are not robust, the eigenvectors of noise covariance matrix of a circular array in isotropic noise field are used to redefine the eigenvectors and array manifold vectors with different orders, and the data sampling covariance matrix is reduced in dimension according to the orders. Then, DOA estimation is performed using the minimum variance distortionless response(MVDR) with the new array manifold vectors and dimension-reduced data sampling covariance matrix. Simulation results show that if there is no error, the highest-order DOA estimation result is consistent with that of the traditional MVDR method. When amplitude and phase errors exist, the more robust low-order DOA estimation results are better than that of the traditional MVDR method, which indicates that the proposed method improves the anti-error robustness, and greatly reduces the amount of computation of matrix inversion. A 12-element uniform circular array is used in the experiment conducted in the sea trail, and the results of the 2nd- and 3rd-order DOA estimation are better than that of the traditional MVDR method, verifying the effectiveness of the proposed method. This DOA estimation method may contribute to applications of circular arrays to unmanned undersea systems.
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