Steady self-adaptive beamforming method based on information source number constraint

Abstract

The invention discloses a steady self-adaptive beamforming technology based on information source number constraint and used for reconstructing covariance matrix and accurately estimating expected signal steering vector. The method comprises the following steps: firstly estimating interference source number, expected signal angle region, interference angle region and noise angle region, estimatingnoise average power in the noise angle region to acquire a noise covariance matrix; estimating accurate interference power through the noise average power in the interference region, reconstructing the interference covariance matrix, and modifying the reconstructed interference covariance matrix according to the interference source number constraint to acquire the interference and noise covariance matrix; estimating the accurate expected signal power through the noise average power in the expected signal angle region, reconstructing the expected signal covariance matrix, decomposing the feature of the expected signal covariance matrix to acquire the steering vector of the expected signal, thereby acquiring the weight vector of the steady self-adaptive beam-former, and forming the output of the steady self-adaptive beam-former.

Description

technical field [0001] The invention relates to the field of array signal processing, in particular to a robust adaptive beamforming method based on covariance moment reconstruction based on interference source number constraints and accurate estimation of desired signal steering vectors. Background technique [0002] Recently, a series of robust adaptive beamforming methods have been proposed. Representative robust methods include: linearly constrained minimum variance method, diagonal loading method, characteristic subspace method, uncertain set method, worst-case performance optimization method, etc. However, considering the selection of parameters, uncertain sets, and algorithm complexity, these robust adaptive beamforming algorithms do not perform well when there are errors in the array model. [0003] In recent years, the robust adaptive beamforming method based on covariance matrix reconstruction has good performance when there are various errors in the array, and has...

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Problems solved by technology

This method mainly uses the Capon space power spectrum to integrally reconstruct the interference plus noise covariance matrix in the angle region of the undesired signal, and then uses the optimization algorithm to estimate the steering vector of the desired signal through the matrix, but the reconstruction method directly uses The Capon space power spectrum integrates the angle variable in the undesired signal angle region. Since the noise is distributed throughout the space, the reconstructed interference plus noise covariance matrix is ​​not accurate enough, which limits the robustness of the algorithm.

Then, a method for reconstructing the interference-plus-noise covariance matrix for any type of array error is proposed. This method mainly changes the original linear integration region and transforms it into a spatial circular uncertainty set. However, the influence of noise is not considered, and a more accurate interference-plus-noise covariance matrix cannot be obtained, and the computational complexity is high

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