摘要
针对压缩感知应用于UWB通信信道估计时信息算子的相干性严重影响UWB信道估计精度的问题,提出将优化的信息算子和贝叶斯算法相结合的方法。该方法以互累积相干参数为准则,首先将加权信息算子通过最优化与正则化得到优化的信息算子,最后通过贝叶斯算法重构UWB信道。理论分析和仿真结果表明优化信息算子的相干性大大减弱,有效提高了UWB信道估计精度,同时该方法能在较低的压缩比和信噪比条件下实现UWB通信信道的准确重建。
The coherence of sensing matrix may affect seriously the estimation accuracy of Ultra-Wideband （ UWB） channel based on Compressive Sensing （ CS ）,thus a method is proposed which combines the optimized sensing matrix with Bayesian algorithm .Taking the cross cumulative coherence as the principle,the optimized sensing matrix is obtained by optimizing and regularizing the weighted sensing matrix .Bayesian algorithm is then used to reconstruct the UWB channel .Both the theoretical analysis and the experimental results show that the optimized sensing matrix has weaker coherence compared with the sensing matrix ,and the estimation accuracy of UWB channel is improved effectively .At the same time,the UWB communication channel can be accurately reconstructed with low compressive ratio and SNR .
出处
《电光与控制》
北大核心
2014年第2期36-40,54,共6页
Electronics Optics & Control
基金
国家自然科学基金(61171170)
关键词
超宽带通信
贝叶斯压缩感知
信息算子
信道估计
相干性
ultra-wideband communication
Bayesian compressed sensing
sensing matrix
channel estimation
coherence
