压缩对称嵌套阵列和稀疏信号重构的近场目标方位估计（英文）

Direction-of-arrival estimation of near-field sources based on compressed symmetric nested array and sparse signal reconstruction

针对现有近场源估计算法中近场源数量受限于阵元数的问题,提出了一种基于稀疏对称嵌套阵列和稀疏信号重构的近场欠定波达方向估计方法。首先利用四阶累积量,将二维空间参数估计问题转化为一维参数估计问题,同时得到差分阵列;为了进一步提高估计分辨率与减少估计误差,对虚拟阵列的接收信号在空间域进行稀疏表示;最后通过L1范数最小二乘法得到目标源的波达方向。相较于现有算法,该方法可以估计更多的目标源,并且有更低的均方误差与更高的分辨率。实验仿真验证了算法的有效性与优越性。

In this paper, a novel underdetermined direction-of-arrival （DOA） estimation method based on compressed symmetric nested array and sparse signal representation is proposed in the near-field. Firstly, the forth order cumulants are employed to transform the original two-dimensional parameter estimation problem into a one-dimensional one and to obtain the difference co-array of the physical array. Then, in order to further increase the angular resolution and reduce the estimate error, the received signals of the virtual array are sparsely represented in spatial domain. Finally, the DOAs of the sources are founded through the use of the L1-regularized least square method. Compared to the existing methods, the proposed approach can process more sources and have lower variance and higher resolution. Simulation results are given to demonstrate the effectiveness and efficiency of the proposed method.