基于贝叶斯预测密度的弱匹配追踪频谱检测  

Spectrum detection algorithm using weak matching pursuit based on Bayesian predictive densities

作　　者：赵知劲[1,2] 胡伟康[1] 

机构地区：[1]杭州电子科技大学通信工程学院,杭州310018 [2]中国电子科技集团第36研究所通信系统信息控制技术国家级重点实验室,浙江嘉兴314001

出　　处：《计算机应用研究》2015年第7期2119-2122,共4页Application Research of Computers

基　　金：电科院预研基金资助项目(41101040102);浙江省研究生创新科技项目(YK2011062)

摘　　要：针对在低信噪比、观测点数较少情况下稀疏度的欠估计问题,提出了一种基于贝叶斯预测密度的弱匹配追踪频谱检测算法。该算法利用贝叶斯预测密度理论推导出罚函数,然后引入弱匹配策略于Co Sa MP算法,提高频谱支撑集估计性能,且减弱受稀疏度估计准确度的影响。仿真结果表明,当信噪比高于3 d B时,利用400个观测样本该算法就能获得90%以上的频谱检测概率,宽带频谱感知性能优于已有算法。To solve the problem of under-estimation of sparsity using a few of observation data with low signal to noise ratio, this paper proposed a spectrum detection algorithm using weak matching pursuit based on Bayesian predictive densities. Firstly, the algorithm derived a penalty function by applying Bayesian predictive densities. Then, it applied the weak matching strategy to the algorithm of CoSaMP, which could enhance the estimation performance of spectrum support set and reduce the influence of estimation error of sparsity. Simulation results show that the proposed algorithm has better performance than other algorithms, as the detection probably of proposed algorithm can be more than 90% when the SNR is higher than 3 dB.

关 键 词：宽带频谱感知 贝叶斯预测密度 稀疏度 弱匹配追踪 

分 类 号：TN911.23[电子电信—通信与信息系统]