Robust Beamforming Method Based on IAA-Based Interference-plus-Noise Covariance Matrix Reconstruction

Abstract

The invention belongs to the technical field of antenna beamforming, and discloses an IAA-based interference plus noise covariance matrix reconstruction robust beamforming method. Firstly, the IAA method is used to estimate the direction of the signal more accurately in an iterative manner, and then the sampling covariance is selected. The eigenvector with the greatest correlation between the eigenvectors of the matrix and the signal steering vector calculated from the estimated expected signal direction is used as the expected signal component, and it is removed from the sampling covariance matrix. Finally, in order to prevent the covariance after removing the signal The variance matrix is ​​singular, and the matrix is ​​loaded diagonally by selecting an appropriate diagonal loading factor, which improves the output signal-to-interference-noise ratio of the adaptive beamformer when the sample contains the desired signal, and in the case of steering vector mismatch The robust performance of the adaptive beamformer is improved.

Description

technical field [0001] The invention belongs to the technical field of antenna beamforming, in particular to an IAA (Iterative Adaptive Approach, Iterative Adaptive) based interference-plus-noise covariance matrix reconstruction robust beamforming method, which is suitable for solving the problem of self-adaptation caused by strong expected signal power in sampling samples The beamformer output signal-to-interference-noise ratio declines, and the adaptive beamformer has good robust performance in the case of steering vector mismatch. Background technique [0002] Adaptive beamforming technology is an important research direction in the field of array signal processing. Compared with the traditional single antenna, the antenna array can calculate the weighting factors on each array element through an adaptive algorithm according to the change of the signal environment, so as to perform spatial filtering on the signal received by the array, so that the formed beam points to th...

AI Technical Summary

Problems solved by technology

In 2003, Vorobyov proposed the worst performance optimization algorithm, which constrains the mismatched signal steering vector in a spherical uncertainty set, and transforms the problem of solving the adaptive weight vector into a second-order cone programming problem , can maintain a certain robustness within a certain range of steering vector mismatch, but the selection of the constraint range of this algorithm affects the performance of the beamformer too much

In recent years, some improved beamforming algorithms based on existing algorithms have been proposed. For example, in 2012, Gu proposed a beamforming method for reconstruction of interference plus noise covariance matrix, but the impact of low snapshot number on algorithm performance is too big

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