Linear-complexity Massive MIMO target space orientation estimation method and device

Abstract

The invention discloses a linear-complexity Massive MIMO target space orientation estimation method and device. The method comprises that a reception signal of a target is collected by an antenna array; an autocorrelation covariance matrix is generated according to the reception signal; a skeleton is extracted from the autocorrelation covariance matrix to generate a low-rank characteristic matrix;low-rank symmetric approximate decomposition is carried out on the autocorrelation covariance matrix based on the low-rank characteristic matrix to generate a low-rank symmetric approximate decomposition matrix; a singular value of the low-rank symmetric approximate decomposition matrix is decomposed, signal sub-space approximate estimation of the reception signal is obtained according to a decomposition result, and a MUSIC spectrum is calculated according to the signal sub-space approximate estimation; and the space orientation of the target is calculated according to a peak value position of the MUSIC spectrum. The random matrix sampling and low-rank approximate decomposition methods are used to avoid direct SVD of the high-dimensional autocorrelation matrix, the computing complexity inthe method is reduced greatly, and the problem of limitation in radar target detection and signal processing in the large-scale antenna scene is solved.

Description

technical field [0001] The present invention relates to the technical field of target detection and space orientation estimation, in particular to a linear complexity Massive MIMO target space orientation estimation method and device. Background technique [0002] With the continuous development of hardware integration technology and the increasingly common demand for high-precision target estimation, large-scale antenna configuration has become the general trend in radar systems and future communication systems. In many application scenarios of radar signal processing and target estimation, as well as wireless communication device positioning, it is usually necessary to use the MUSIC (Multiple Signal Classification) method to achieve high-precision target detection and spatial orientation estimation. The MUSIC spectrum estimation method has extensive and important applications in multi-channel radar systems and large-scale antenna communication systems, and can achieve high...

AI Technical Summary

Problems solved by technology

[0003] In practical applications, SVD decomposition requires extremely high computing resources and processing delays. For future large-scale antenna element M scenarios (M>200), the computational complexity of full-rank SVD decomposition is about It will not be able to meet the real-time and low complexity requirements of high-precision target detection and signal processing scenarios

In order to reduce the high computational complexity required for full-rank SVD decomposition, another K-rank SVD decomposition method can be used, that is, only focus on and calculate the singular vectors corresponding to the first K singular values, thus reducing the calculation to a certain extent complexity, but its complexity is roughly Recently, some studies have proposed to use the matrix inversion method to replace the SVD decomposition in the MUSIC spectrum estimation method, which can reduce the computational complexity and processing delay, but the matrix inversion still requires a high computational complexity (not less than the square complex degrees), and its MUSIC spectrum estimation performance will be significantly affected

To sum up, the computational complexity of the existing MUSIC spectrum estimation method remains at the quadratic complexity, so it will still not be applicable in real-time processing scenarios under large-scale antenna configurations, such as in-vehicle millimeter-wave radar and high-speed large-scale antenna Communication and other application scenarios

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