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Two-Dimensional DOA Estimation for Incoherently Distributed Sources with Uniform Rectangular Arrays

机译:具有均匀矩形阵列的非相干分布源的二维DOA估计

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摘要

Aiming at the two-dimensional (2D) incoherently distributed (ID) sources, we explore a direction-of-arrival (DOA) estimation algorithm based on uniform rectangular arrays (URA). By means of Taylor series expansion of steering vector, rotational invariance relations with regard to nominal azimuth and nominal elevation between subarrays are deduced under the assumption of small angular spreads and small sensors distance firstly; then received signal vectors can be described by generalized steering matrices and generalized signal vectors; thus, an estimation of signal parameters via rotational invariance techniques (ESPRIT) like algorithm is proposed to estimate nominal elevation and nominal azimuth respectively using covariance matrices of constructed subarrays. Angle matching method is proposed by virtue of Capon principle lastly. The proposed method can estimate multiple 2D ID sources without spectral searching and without information of angular power distribution function of sources. Investigating different SNR, sources with different angular power density functions, sources in boundary region, distance between sensors and number of sources, simulations are conducted to investigate the effectiveness of the proposed method.
机译:针对二维(2D)非相干分布(ID)源,我们探索了基于均匀矩形阵列(URA)的到达方向(DOA)估计算法。通过转向矢量的泰勒级数展开,首先在较小的角展布和较小的传感器距离的前提下,推导了子阵列之间标称方位角和标称仰角的旋转不变性关系。然后,可以通过广义导引矩阵和广义信号矢量来描述接收到的信号矢量。因此,提出了一种通过旋转不变技术(ESPRIT)之类的算法对信号参数进行估计的方法,以利用构造的子阵列的协方差矩阵分别估计标称仰角和标称方位角。最后根据Capon原理提出了角度匹配方法。所提出的方法可以估计多个2D ID源,而无需频谱搜索,也不需要源的角功率分布函数信息。研究了不同的信噪比,具有不同角功率密度函数的信号源,边界区域的信号源,传感器之间的距离以及信号源的数量,并通过仿真研究了该方法的有效性。

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