A Target Arrival Angle Estimation Method Based on Dynamic Update of Spatial Discrete Grid

Abstract

The invention provides a method for estimating the target angle of arrival based on the dynamic update of spatial discrete grids, mainly for solving the problem of spatial discrete grid mismatch in the DOA estimation process on the array signal processing problem, which belongs to the array signal processing problem. technical field. The eigenvalue decomposition of the signal sampling covariance matrix is ​​carried out, and the discrete point closest to the real angle is found by the method of basis tracking, and then the discrete grid points are dynamically updated through iteration, so that the discrete grid points continuously approach the real DOA value of the target , so as to accurately estimate the arrival angles of multiple targets. The present invention effectively solves the problem of spatial discrete grid adaptation by using the method of spatial discrete grid dynamic learning, so that the algorithm can still estimate DOA quickly and maintain a very high level even when the spatial discrete grid step size is large. High target estimation accuracy.

Description

technical field [0001] The invention relates to a method for estimating a target arrival angle on a uniform linear array based on dynamic update of a space discrete grid, and belongs to the technical field of array signal processing. Background technique [0002] Array signal processing technology uses multiple sensors to measure signals, thereby obtaining additional spatial degrees of freedom by using the spatial characteristics of signals. 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, the array signal processing technology has been successfully applied in the fields of astronomical observation, radar, sonar, communication system and biomedicine. Array signal processing technology mainly includes adaptive beamforming technology and high-resolution spatial spectrum estimation technology. The estimation of the direction of the ta...

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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