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Low-angle Estimation Method via Sparse Bayesian Learning |
ZHANG Yongshun①② GE Qichao① DING Shanshan① GUO Yiduo① |
①(Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China)
②(Collaborative Innovation Center of Information Sensing and Understanding, Xi’an 710077, China) |
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Abstract In order to improve the accuracy of low-angle estimation in meter-wave radars, combined with sparse Bayesian learning, this paper makes use of the Kronecker product and the similarity of the sparse structure between adjacent snapshots to transform the multiple measurement vector model into a single measurement vector model. The angle of the source is obtained by the coefficient matrix of the sensing matrix related to signal and the coefficient matrix is recovered by the continuous iteration in sparse Bayesian learning. Simulation experiments show that the proposed method has better performance than the generalized MUSIC algorithm and M-FOCUSS algorithm, the influence on algorithm performance caused by the snapshot change is obtained.
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Received: 25 November 2015
Published: 12 June 2016
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Fund: The National Natural Science Fouudation of China (61372033, 61501501) |
Corresponding Authors:
GE Qichao
E-mail: geqichao927@163.com
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