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.
出处
《计算机应用研究》
CSCD
北大核心
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)
关键词
宽带频谱感知
贝叶斯预测密度
稀疏度
弱匹配追踪
wideband spectrum sensing
Bayesian predictive densities
sparsity
weak matching pursuit
