Target arrival angle estimation method based on spatial discrete grid dynamic update

Abstract

The invention provides a target arrival angle estimation method based on spatial discrete grid dynamic update, and belongs to the technical field of array signal processing. The target arrival angle estimation method is mainly for solving the problem of spatial discrete grid mismatch faced in the DOA estimation process on an array signal processing problem. Eigenvalue decomposition is performed ona signal sampling covariance matrix, through a basis pursuit method, a discrete point closest to a real angle is found, then the discrete grid points are dynamically updated by iteration, so that thediscrete grid points continuously approach a true DOA value of a target, and accurate estimation of multiple target arrival angles is carried out. The target arrival angle estimation method based onthe spatial discrete grid dynamic update effectively solves the problem of the spatial discrete grid mismatch by using a spatial discrete grid dynamic learning method, so that the algorithm can quickly estimate the DOA and keep a quite high target estimation accuracy even if the step size of a spatial discrete grid is large.

Description

technical field [0001] The invention relates to a method for estimating an angle of arrival of a target based on a dynamic update of a space discrete grid on a uniform linear array, and belongs to the technical field of array signal processing. Background technique [0002] Array signal processing technology uses multiple sensors for signal measurement, thereby using the spatial characteristics of the signal to obtain additional spatial domain freedom. Compared with traditional one-dimensional signal processing technology, it has the advantages of higher signal gain, strong anti-interference, and high resolution. . After years of development, array signal processing technology has been successfully applied in fields such as astronomical observation, radar, sonar, communication system and biomedicine. Array signal processing technologies mainly include adaptive beamforming technology and high-resolution spatial spectrum estimation technology. Estimation of target angle of a...

AI Technical Summary

Problems solved by technology

Usually, in order to avoid or reduce the influence of discrete grid errors as much as possible, it is necessary to use dense spatial grid division as much as possible. However, dense grid division cannot fully guarantee that the real target arrival angle falls on the discretely divided grid points. Moreover, as the discrete grid division becomes denser, the large increase in the number of discrete grid points will increase the computational complexity

In the spectral search algorithm, a finer search is usually performed near the spectral peak obtained on the initial grid, which will improve the discrete grid mismatch problem of the search algorithm to a certain extent, but it cannot fundamentally eliminate discrete grid error

Sparse reconstruction algorithms mainly construct sparse dictionaries on finite discrete spatial grids. Similarly, no matter how dense the network division is, it cannot guarantee that the target is included in the discrete grid. In addition, too dense spatial grids will lead to sparse Increased relevance of dictionaries

According to the constraint equidistant condition, the recovery effect of the sparse reconstruction algorithm is the best when the correlation of the sparse dictionary is low. As the correlation of the column vector of the sparse dictionary increases, the recovery effect of the sparse reconstruction algorithm will become worse.

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