A Reduced-Rank Beamforming Method Based on Joint Alternating Optimization

Abstract

The invention discloses a reduced rank beam forming method based on united alternative optimization. The method mainly aims to solve the problems that a full-dimensional adaptive beam former is heavy in computation and low in output SINR under a small sample condition. The implementation process of the method includes the steps that an array antenna receives data and calculates a sample covariance matrix; the sample covariance matrix updates a prior covariance matrix by the utilization of the spatial spectrum reconstruction technology; an estimation covariance matrix of array data is obtained by the adoption of a weight fusion processing method; an optimal dimensionality reduction matrix and an optimal dimensionality reduction weight vector are obtained through the estimation covariance matrix according to the linear linearly constrained minimum variance error criterion in the manner of adopting a united alternative optimization dimensionality reduction matrix and a dimensionality reduction weight vector. The method has the advantages of being light in computation burden and high in output SINR, and is used for estimating the covariance matrix under the small sample condition and forming an optimal reduced rank beam.

Description

technical field [0001] The invention belongs to the field of array signal processing, and relates to adaptive beamforming technology, in particular to a reduced-rank beamforming method using joint iterative optimization combined with spatial spectrum reconstruction and weighted fusion processing, which is used to improve The output signal-to-interference-noise ratio of the adaptive beamformer reduces the computational complexity. Background technique [0002] Adaptive beamforming is a hot research direction in the field of array signal processing, and has important application value in radar, sonar, communication, earthquake monitoring and other fields. In engineering practice, full-dimensional adaptive processing requires a large number of training samples, and the amount of calculation increases cubically with the increase of the processor dimension. However, it is difficult to obtain a large number of training samples in practice, and the large amount of calculation has a...

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Problems solved by technology

Orthogonal projection algorithms need to accurately estimate the signal subspace, and under the condition of small samples, the accuracy of subspace estimation is limited; Ding Qianjun et al published the article "In adaptive array In "Effective Realization Algorithm of Multi-Stage Wiener Filter", a new effective realization algorithm of multi-stage Wiener filter is proposed. Rank subspace, also it is particularly difficult to determine the processor or subspace dimension under small sample conditions, and it is impossible to adjust the dimensionality reduction matrix according to the output of the beamformer

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