In this paper, a novel method called fourth-order spatiotemporal algorithm (FOSA) is presented for classification, 2D DOA estimation and 3D localization of mixed near-field and far-field sources using a uniform circular array with a center sensor. By constructing two spatiotemporal cumulant matrices, without range information, and regarding the fourth-order stationary property, the 2D DOA estimation matrix is extracted. By applying the eigenvalue decomposition, the virtual steering matrix is estimated. The sensor in the center of the array is used to extract the valid virtual steering vectors. The 2D DOAs are estimated by a least-squares method and without the use of spectral search. Ranges of near-field sources are estimated by constructing a spatial cumulant matrix and using the 1D MUSIC technique. FOSA provides a reasonable classification of signals types. It can detect near and far field sources possessing the same azimuth or elevation DOAs. Furthermore, it has a much low computational complexity. FOSA prevents array aperture loss without any pairing parameters. The simulation results validate its high performance in terms of estimation accuracy, resolution and the probability of correct classification of the signals types.

在本文中，提出了一种新的方法，称为四阶时空算法（FOSA），用于使用带有中心传感器的均匀圆形阵列对混合的近场和远场源进行2D DOA估计和3D定位。通过构造两个没有时域信息的时空累积量矩阵，并且关于四阶平稳特性，提取2D DOA估计矩阵。通过应用特征值分解，可以估算虚拟转向矩阵。阵列中心的传感器用于提取有效的虚拟转向矢量。2D DOA是通过最小二乘法估计的，并且不使用频谱搜索。通过构建空间累积矩阵并使用一维MUSIC技术估算近场源的范围。FOSA提供了信号类型的合理分类。它可以检测具有相同方位角或仰角DOA的近场和远场源。此外，它具有非常低的计算复杂度。FOSA无需任何配对参数即可防止阵列孔径损失。仿真结果在估计精度，分辨率和信号类型正确分类的概率方面证明了其高性能。