A modeling method for m-uca estimation of m-1 sources

Abstract

The invention discloses a modeling method for estimating M-1 signal sources by a uniform circular array of M array elements. The M-UCA is placed arbitrarily in the far field of a colored noise environment, and the incident The instantaneous phase is obtained by Hilbert-Huang transformation of the array signal of angle, and the instantaneous phase covariance matrix is ​​constructed to obtain its eigenvalues; then, these eigenvalues ​​are put into the support vector machine for training to obtain a mathematical model. Experiments show that the mathematical model obtained by using this modeling method can improve the problem of poor estimation accuracy of M-UCA estimating M-1 sources under the background of spatial color noise, and the estimation accuracy is determined by the incidence angle of the source, the signal-to-noise ratio, and the number of beats. The impact is small and has a certain degree of universality. The invention is applied to the estimation technology of the number of information sources in the technical field of array signal processing.

Description

technical field [0001] The invention relates to a technique for estimating the number of information sources under the background of colored noise applied in the technical field of array signal processing. Background technique [0002] Accurately knowing the number of sources has always been the prerequisite for accurate estimation of the direction of arrival (DOA) of a signal. If the estimated number of sources is inconsistent with the actual number of sources, the direction finding performance will be seriously degraded. At present, conventional source number estimation algorithms are proposed based on the background noise being white noise and the background noise being colored noise respectively. Methods based on white noise background generally use the eigenvalues ​​and eigenvectors of the covariance matrix of the original data, but this type of algorithm is not suitable for the case where the background noise is colored noise. [0003] Diagonal loading method and Gail...

AI Technical Summary

Problems solved by technology

However, according to research, most of the existing algorithms assume that the number of sources is at least half of the number of array elements. When the number of sources is close to the number of array elements, especially when the number of sources is only one less than the number of array elements, the performance will decrease. greatly affected

Zhao Huiqiang et al.'s article "Estimation Algorithm for the Number of Signal Sources Based on Virtual Array [J]. Firepower and Command and Control, 2010, 35(12): 156-158." Based on the Kullback criterion, a virtual array Gael The circle improvement method can improve the unsatisfactory estimation effect when the number of sources is close to the number of array elements, but it is only verified in the background of white noise. The experiment shows that the estimation effect is not ideal in the background of colored noise.

The article "Improved Source Number Detection and Direction Estimation With Nestd Arrays and ULAs Jackknifing[J]. IEEE Trans onSignal Processing, 2013, 61(23): 6118-6128." by Han Keyong et al. proposed an idea of ​​embedded arrays, using In order to improve the degree of freedom of the system, it solves the problem that the actual number of sources is close to or greater than the number of array elements and the estimation effect is poor, but this method is proposed under the background of white noise

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