Self-adaptive beam forming method based on covariance reconstruction and guide vector compensation

Abstract

The invention discloses a self-adaptive beam forming method based on covariance reconstruction and guide vector compensation. According to the method, in the situation that desired signals exist in sampling snapshot and signal guide vectors are mismatched, interference can be effectively inhibited, main lobe shape preservation and side lobes of a self-adaptive directional diagram can be reduced, and high output SINR and quick convergence speed can be obtained. According to the method, eigenvalue decomposition is carried out on a sampling covariance matrix, noise sub space is estimated by means of an MDL criterion, the incident angle of a signal source is estimated in a Root-MUSIC method, the incident angle of the desired signals is judged, and then a new interference and noise covariance matrix is reconstructed; mismatching of the guide vectors of the desired signals is compensated by solving a quadratic programming problem with quadratic constraints; ultimately, a self-adaptive weight vector is solved by means of the reconstructed new interference and noise covariance matrix and the corrected guide vectors, a null is formed in the interference direction in a self-adaptive mode, and interference is effectively inhibited.

Description

technical field [0001] The invention relates to the technical field of array signal processing, in particular to an adaptive beamforming method based on covariance matrix reconstruction and steering vector error compensation. Background technique [0002] Array signal processing is an important branch in the field of signal processing. It has been widely used in many military and national economic fields such as radar, sonar, communication, navigation, biomedical engineering, voice signal processing, and earthquake monitoring. Adaptive beamforming is an important research part of array signal processing. Its essence is to form a null at the interference position and get a peak in the direction of the desired signal by adaptively weighting each array element, so as to enhance the desired signal and suppress interference. signal and the purpose of attenuating the noise signal. Among them, Minimum Variance Distortionless Response (MVDR) is a commonly used adaptive beamforming ...

AI Technical Summary

Problems solved by technology

When the number of sampling snapshots is low, the output SINR of the SMI algorithm and the convergence speed of the adaptive pattern are slow. If there is an error in the signal steering vector, the output SINR of the SMI algorithm will be lost and the adaptive pattern will be distorted.

The more serious situation is that when the sampling snapshot contains the desired signal, it will cause zero traps in the direction of the desired signal, resulting in the cancellation of the desired signal and a serious drop in the output signal-to-interference-noise ratio, which makes the output SINR of the SMI algorithm drop sharply. The pattern is severely deformed, the main lobe is deformed, and the side lobes are elevated

Therefore, when the sampling snapshot contains the desired signal and its steering vector does not match, the SMI algorithm will not be able to effectively suppress the interference and enhance the desired signal

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