Direction-of-arrival estimation method based on sample covariance matrix sparsity

Abstract

The invention discloses a direction-of-arrival estimation method based on sample covariance matrix sparsity, and relates to the field of array signal processing. The method comprises the steps that firstly, dimensionality reduction is carried out on a covariance matrix, receiving signals, of a radar antenna array, and the covariance matrix, receiving the signals, of the radar antenna array is obtained after dimensionality reduction; secondly, a sparse vector cost function based on sparse constraints is built according to the covariance matrix after dimensionality reduction; thirdly, the sparse vector cost function based on the sparse constraints is constructed to be the mode suitable for convex programming package solving, and a sparse vector is solved according to the mode of convex programming package solving; fourthly, a non-zero element in the sparse vector is determined as a target azimuth angle, and the target azimuth angle is a target arrival direction. The method mainly solves the problems that in the prior art, known noise power is needed, and the calculated amount is large, and the method is mainly used for scenes of array signal processing.

Description

technical field [0001] The invention belongs to the field of radar technology, relates to the field of array signal processing, in particular to a method for estimating a direction of arrival based on the sparseness of a sample covariance matrix. Background technique [0002] In recent decades, the direction of arrival (DOA) estimation of far-field narrowband signals has been a hot issue in array signal processing, and has been widely used in many fields such as radar, electromagnetic field, wireless communication, medical imaging, and seismic exploration. . The main goal of DOA estimation is to detect and estimate the orientation of multiple signals in a noisy environment. Aiming at the problem of DOA estimation, a large number of DOA estimation methods have been proposed, including: beam forming (Beam Forming, BF) method, multiple signal classification (Multiple Signal Classifcation, MUSIC) method based on subspace method and minimum variance without distortion (Minimum v...

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Problems solved by technology

[0004] In view of the deficiencies of existing DOA estimation methods, if only non-correlation signals can be processed, correlative signals need to be de-correlated, the amount of calculation is large, regularization parameters need to be determined and the selection of regularization parameters is difficult, and the noise power needs to be known. The present invention A new DOA estimation method based on the sparsity of the sample covariance matrix is ​​proposed, which improves the traditional DOA estimation model based on the sparsity of the covariance matrix, and does not require known or estimated noise power, robustness and computational efficiency have been promoted

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