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Eigenvector-based Linearly Constrained Minimum Variance Adaptive Pattern Control Algorithm


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Eigenvector-based Linearly Constrained Minimum Variance Adaptive Pattern Control Algorithm
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
adaptive pattern control eigenvectors quiescent patterns
TN911. 7
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.


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Last Update: 2012-10-23