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基于样本协方差矩阵谱分离特性的波达方向估计方法 被引量:2

A direction of arrival estimation method based on spectral separation of the sample covariance matrix
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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。仿真与水池实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,两个相邻目标可以正确分辨,而MUSIC算法则有伪目标出现。 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.
作者 郭拓 王英民 张立琛 GUO Tuo;WANG Yingmin;ZHANG Lichen(School of Marine Science and Technology-,Northwestern Polytechnical University,Xi'an 710072,China)
出处 《振动与冲击》 EI CSCD 北大核心 2018年第12期23-28,47,共7页 Journal of Vibration and Shock
基金 国家自然科学基金(61671378)
关键词 样本协方差矩阵(SCM) 谱分离特性 主特征空间 波达方向估计(DOA) 小快拍 sample covariance matrix (SCM) spectral separation main feature space direction of arrival (DOA) estimation small number of snapshots
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