基于快速变分稀疏贝叶斯学习的频谱感知与定位

针对稀疏贝叶斯压缩感知算法存在复杂度高、收敛速度慢等缺陷,提出了一种快速变分稀疏贝叶斯学习的频谱检测与定位算法.该算法在原始问题求解过程中增加了辅助变量,消除了原问题模型中未知变量之间耦合度高的问题.并依据稀疏参数的收敛情况,自适应删除不收敛稀疏参数对应的基函数,从而进一步加快了算法的收敛速度.实验结果表明:该算法在收敛速度和频谱检测精度上有显著的改善.

Spectrum Sensing and Location Based on Fast Variational Sparse Bayesian Learning

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 .