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Date of Publication:2022-10-04

Journal:电讯技术

Issue:5

Page Number:584-588

ISSN No.:1001-893X

Abstract:To improve the performance reduction of sample matrix inversion( SMI) algorithm as the desired signal exists in training data,a modified interference-plus-noise covariance matrix reconstructing( CMR) algorithm is proposed in this paper. The algorithm firstly uses the eigenvector corresponding to the mini-mum eigenvalue of the sample autocovariance matrix to structure the space distribution coefficient,and then accumulates it on the range except the direction of the desired signal to reconstruct the interference-plus-noise covariance matrix. In the presence of coherent signals,the element in the max eigenvector can be uti-lized to make the covariance matrix a Toeplitz matrix,and then the CMR algorithm( Toeplitz CMR,TCMR) can be used. Simulation and experiment results demonstrate that the CMR algorithm applied in incoherent signal circumstance and the TCMR algorithm applied in coherent signal circumstance have better output performance.

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