CAJ | 학술논문

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。
침대희소패협사압축감지산법존재복잡도고、수렴속도만등결함，제출료일충쾌속변분희소패협사학습적빈보검측여정위산법。해산법재원시문제구해과정중증가료보조변량，소제료원문제모형중미지변량지간우합도고적문제。병의거희소삼수적수렴정황，자괄응산제불수렴희소삼수대응적기함수，종이진일보가쾌료산법적수렴속도。실험결과표명：해산법재수렴속도화빈보검측정도상유현저적개선。
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 .