中南民族大学学报（自然科学版）Issue(1)：62-66,5.
基于快速变分稀疏贝叶斯学习的频谱感知与定位
Spectrum Sensing and Location Based on Fast Variational Sparse Bayesian Learning
摘要
Abstract
Based upon the fact that sparse Bayesian compressed sensing algorithm has the defects of high complexity and slow convergence speed , a spectrum sensing and location algorithm based on fast variational sparse Bayesian learning is proposed.The algorithm adds some auxiliary variable in the process of solving original problem , which eliminates the high coupling coefficient between the unknown variables in the original model .At the meantime, the algorithm can adaptively delete the basic functions corresponding to un-convergence sparse parameters according to the converging conditions of the sparse parameters , thus leading to the effect that the velocity of convergence is further accelerated .The experimental results show that the algorithm significantly improves the accuracy and speed of sensing .关键词
认知无线电/频谱感知/变分稀疏贝叶斯学习/压缩采样Key words
cognitive radio/spectrum sensing/variational sparse Bayesian learning/compressive sampling分类
信息技术与安全科学引用本文复制引用
朱翠涛,刘绪杰..基于快速变分稀疏贝叶斯学习的频谱感知与定位[J].中南民族大学学报（自然科学版）,2014,(1):62-66,5.基金项目
国家自然科学基金资助项目 （）
