Robust adaptive beam forming method based on non-Gaussian non-circular signal characteristics

Abstract

The invention discloses a robust adaptive beam forming method based on non-Gaussian non-circular signal characteristics, which comprises the following steps of: acquiring an initial direction of arrival angle of an expected signal and interference by utilizing a Capon power spectrum, dividing a whole space into an expected signal area, an interference area and a noise area, and estimating noise power by utilizing a Capon power average value in the noise area; acquiring nominal steering vectors of expected signals and interference; the feature vector of the sampling covariance matrix of the uniform linear array receiving data is used as an error vector perpendicular to the nominal steering vector; performing steering vector updating based on a Capon power maximization criterion, and estimating to obtain a steering vector of a desired signal and interference; reconstructing an interference and noise covariance matrix, and estimating non-circular coefficients of an expected signal and interference; and reconstructing a pseudo covariance matrix, expanding an expected signal steering vector and expanding a covariance matrix, and performing beam forming on signals received by the array based on the expanded expected signal steering vector and the covariance matrix.

Description

technical field [0001] The invention relates to the field of beamforming research in the field of array signal processing, in particular to a robust adaptive beamforming method based on non-Gaussian non-circular signal characteristics. Background technique [0002] Traditional well-known robust adaptive beamforming algorithms include: diagonal loading algorithms, eigensubspace-based algorithms, uncertain set-based algorithms, etc., but in practice, there are various errors that lead to these types of beamforming algorithms The performance of the forming algorithm has a more obvious decline. In recent years, algorithms based on covariance matrix reconstruction have received extensive attention and have achieved better performance than traditional algorithms. However, this type of algorithm is a general algorithm and does not take into account the characteristics of the signal to further improve the performance of the beamformer, so the performance of this type of algorithm i...

AI Technical Summary

Problems solved by technology

However, this type of algorithm is a general algorithm and does not take into account the characteristics of the signal to further improve the performance of the beamformer, so the performance of this type of algorithm is always worse than the theoretically optimal Capon beamformer or the minimum variance without distortion Response (Minimum Variance Distortionless Response, MVDR) Beamformer Performance

At the same time, this type of algorithm needs to construct a convex optimization problem to complete the estimation of the desired signal steering vector, and the computational complexity is relatively large

In addition, people are increasingly demanding the performance of the beamformer, and most algorithms that use the characteristics of the signal to improve the performance of the beamformer still need to solve the convex optimization problem, there is still a problem of high computational complexity, and limited performance

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