基于样本协方差矩阵谱分离特性的波达方向估计方法

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (12) : 23-28.

论文

郭拓，王英民，张立琛

作者信息 +

A direction of arrival estimation method based on spectral separation of the sample covariance matrix

GUO Tuo,WANG Yingmin,ZHANG Lichen

Author information +

文章历史 +

摘要

小快拍条件下利用样本协方差矩阵代替统计协方差矩阵会带来较大误差，导致传统DOA估计算法不能准确估计目标方位。通过分析发现在不同阵元数与快拍数之比情况下，不管相干源还是独立源，样本协方差矩阵都具有明显的谱分离特性，在此基础上提出了采用小快拍的主特征空间目标波达方向估计方法，该方法利用导向向量与噪声子空间正交，且与信号子空间平行的特性，使用导向向量与主特征空间相乘再取反余弦构造出目标DOA估计幅度。仿真与水池试验中阵元数与样本数之比为1时依然可以准确将多个目标分辨出；海试数据验证中，阵元数与样本数之比也同样为1时，2个相邻目标可以正确分辨出，而MUSIC算法则有伪目标出现。

Abstract

Sample covariance matrix (SCM) with small sample instead of a array covariance matrix will bring great error, which leads to the traditional algorithm can not accurately estimate the direction of arrival (DOA) of targets. It is found that the sample covariance matrix has obvious spectral separation property with different ratio of elements number to samples number regardless of coherent source or independent source, and then a DOA estimation method based on main feature space was proposed using small number of snapshots. It is well known that the steering vector is orthogonal to the noise subspace and parallel to the signal subspace. Steering vector and the main feature space of SCM were multiplied, and then the inverse cosine was taken to construct the targets DOA estimation amplitude. In the simulation and water tank experiment, when the ratio of the number of sensors to the number of samples is 1, the proposed method can still distinguish multi targets correctly; in the sea trial, when the above ratio is 1, it can identify 2 adjacent targets clearly, while the MUSIC algorithm has a pseudo target.

导出引用

郭拓，王英民，张立琛. 基于样本协方差矩阵谱分离特性的波达方向估计方法[J]. 振动与冲击, 2018, 37(12): 23-28

GUO Tuo,WANG Yingmin,ZHANG Lichen. A direction of arrival estimation method based on spectral separation of the sample covariance matrix[J]. Journal of Vibration and Shock, 2018, 37(12): 23-28

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