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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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.

Key words： sample covariance matrix(SCM) spectral separation main feature space direction of arrival(DOA) estimation small number of snapshots

Received: 06 January 2017 Published: 15 June 2018

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	Articles by authors
	GUO Tuo
	WANG Yingmin
	ZHANG Lichen

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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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2018/V37/I12/23

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