基于均匀圆阵伪协方差矩阵的单快拍测向方法

doi:10.12305/j.issn.1001-506X.2021.10.14

摘要/Abstract

摘要：

传统的单快拍测向算法只适用于具有Vandermonde形式的阵列流型, 均匀圆阵的阵列流型不具有Vandermonde矩阵结构, 因此大部分基于线阵的伪协方差矩阵的构造方法无法直接适用于均匀圆阵。针对该问题, 从侦察方的角度对波达方向(direction of arrival, DOA)估计算法进行了研究, 改进了伪协方差矩阵的构造方法, 提出了一种基于均匀圆阵的单快拍测向算法。通过与模式空间前后向平滑(mode forward-backward spatial smoothing, MODE-FBSS)算法的对比仿真, 验证了算法的有效性, 证实了所提方法的性能优于MODE-FBSS算法。

关键词: 伪协方差矩阵, 均匀圆阵, 单快拍, 空间平滑

Abstract:

The traditional single snapshot direction finding algorithm is only applicable to the array manifold with Vandermonde form. And the array manifold of uniform circular array does not have Vandermonde matrix structure, so that most methods of constructing pseudo-covariance matrix which are suitable for linear array, cannot be directly used to solve the related problems based on uniform circular array. To solve this problem, the direction of arrival (DOA) estimation algorithm from the scout's point of view is studied, the method of constructing pseudo-covariance matrix is improved, and a single snapshot direction finding algorithm based on uniform circular array is proposed. Compared with the mode forward-backward spatial smoothing (MODE-FBSS) method, the simulation results show that the algorithm is effective and the performance of the proposed method is better than that of the MODE-FBSS method.

Key words: pseudo-covariance matrix, uniform circular array, single snapshot, spatial smoothing

中图分类号:

TN911

汪鹏, 熊钊, 于俊鹏, 田勇, 李万春. 基于均匀圆阵伪协方差矩阵的单快拍测向方法[J]. 系统工程与电子技术, 2021, 43(10): 2812-2818.

Peng WANG, Zhao XIONG, Junpeng YU, Yong TIAN, Wanchun LI. Single snapshot direction finding method with pseudo-covariance matrix based on uniform circular arrays[J]. Systems Engineering and Electronics, 2021, 43(10): 2812-2818.

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步骤	具体内容	实现结果
1	通过UCA阵元接收的数据构造矩阵	X₀
2	通过式(10)、式(11)计算得出矩阵	J
3	通过式(12)、式(13)计算得出矩阵	F
4	利用步骤2和步骤3得到的矩阵, 由式(15)计算得到预处理矩阵	T
5	完成对接收信号的预处理计算X=TX₀	X
6	利用矩阵X中筛选出的数据, 由式(26)计算得到伪协方差矩阵	${\mathit{\boldsymbol{\tilde R}}} $
7	根据MUSIC算法对$ {\mathit{\boldsymbol{\tilde R}}}$进行特征分解	信号子空间U_S和噪声子空间U_N
8	对方位角θ在[0°, 360°]进行最小优化搜索	目标的精确定位结果

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