|Table of Contents|

Eigenvector-based Linearly Constrained Minimum Variance Adaptive Pattern Control Algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2011年04期
Page:
529-533
Research Field:
Publishing date:

Info

Title:
Eigenvector-based Linearly Constrained Minimum Variance Adaptive Pattern Control Algorithm
Author(s):
LI Hong-tao1HE Ya-peng1ZHU Xiao-hua1HU Wen2
1. School of Electronic Engineering and Optoelectronic Technology,NUST,Nanjing 210094,China; 2. College of Information Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
Keywords:
adaptive pattern control eigenvectors quiescent patterns
PACS:
TN911. 7
DOI:
-
Abstract:
To solve the problem of high sidelobe in the adaptive beamformer, this paper presents an eigenvector-based linearly constrained minimum variance ( E-LCMV ) adaptive pattern control algorithm. The presented algorithm gets the interference subspace and noise subspace by performing eigen decomposition( ED) to covariance matrix of the input data. Utilizing the orthogonality of the subspace, the proposed algorithm amends constraint matrix and constraint response vector of quiescent weighting vector. In this way, the proposed algorithm can restrain interference and make the overall beamformer sidelobe response equal to the desired quiescent response in the condition of small number of snapshots at the same time. Computer simulation shows that the presented algorithm can obtain low sidelobe and improve the output signal to interference plus noise ratio( SINR) in the condition of small number of snapshots, confirming the validity and superiority of the proposed algorithm.

References:

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Last Update: 2012-10-23