Method for estimating source number and incoming wave direction angle based on cross-cyclic correlation MUSIC algorithm in impulse noise environment

Abstract

The invention discloses a method for estimating signal source number and an incoming wave direction angle based on a cross-cyclic correlation MUSIC algorithm in an impulse noise environment, which belongs to the field of array signal processing. The method comprises the following steps: firstly, constructing a far-field narrow-band signal source model comprising M array elements and N far-field narrow-band signals, and calculating received data xm(t) at the time t of the m-th array element. Secondly, for the time t, calculating a cross-cyclic covariance matrix R(m,k) of two array elements by using the received data of the any two array elements; and summing and taking a mean value of MxM cross-cyclic correlation covariance matrices R(m,k), and obtaining a cross-cyclic correlation covariance matrix Rmutual. Finally, performing a singular value decomposition on the cross-cycle correlation covariance matrix Rmutual, and calculating its power spectral density P(theta): searching the localspectral peak of the power spectral density P(theta) and obtaining the estimated value of the incoming wave direction of the far-field narrow-band signal. The method for estimating signal source number and incoming wave direction angle based on cross-cyclic correlation MUSIC algorithm in impulse noise environment utilizes the cyclostationary characteristic of the signal, and the cross-correlationfurther removes a random noise and a clutter component on the basis of the auto-correlation, thereby improving the estimation performance of the incoming wave direction angle in the background of a strong impulse noise.

Description

Technical field [0001] The invention belongs to the field of array signal processing, and specifically relates to a method for estimating the number of signal sources and the direction angle of arrival based on a mutual cyclic correlation MUSIC (Multiple Signal Classification) algorithm under an impulse noise environment. Background technique [0002] The direction of spatial spectrum estimation in the field of array signal processing can be divided into two categories from the processing methods of subspace decomposition algorithms. One is the noise subspace algorithm represented by the MUSIC algorithm, and the other is the rotation invariant algorithm. Space (ESPRIT) is a representative signal subspace algorithm. The algorithms represented by the MUSIC algorithm include feature vector method, MUSIC, root finding MUSIC and MNM (Minimum Norm), etc.; algorithms represented by ESPRIT mainly include TAM (Toeplitz Approximation), LS-ESPRIT ( Least Squares-Rotation Invariant Subspace...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the accuracy is affected by the low-frequency noise introduced by autocorrelation, the present invention proposes a method for estimating the number of sources and the direction angle of incoming waves based on the reciprocal correlation MUSIC algorithm in an impulsive noise environment. High accuracy, is a highly robust angle estimation algorithm

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