摘要
压缩感知(Compressed Sensing)技术通过信号采样与压缩过程同时进行的方式,解决了传统的信号处理方式中信号存储传输空间较大、处理效率较低的问题。信号重构是压缩感知技术中的关键问题之一,贝叶斯压缩感知(Bayesian Compressed Sensing)算法通过稀疏贝叶斯回归模型中的相关向量机来解决压缩感知中的信号重构问题。仿真实验结果表明,贝叶斯压缩感知算法能够以较小的相对误差重构出原始信号,性能比较优越。
The problems of large storage and transmission space of signals and low efficiency of signal processing in traditional signalprocessing methods are solved by Compressed Sensing(CS)technology through carrying out the sampling process and the compressionprocess simultaneously.Signal reconstruction is one of the key problems in compressed sensing technology.Bayesian Compressed Sensing(BCS)algorithm is based on sparse bayesian regression model in the relevance vector machine to solve the problem of signal reconstructionin compressed sensing.The simulation experiment results show that the Bayesian Compressed Sensing algorithm can reconstruct the originalsignal with lower relative error,and the performance of the technology is superior.
出处
《信息技术与信息化》
2016年第12期42-45,共4页
Information Technology and Informatization
关键词
贝叶斯压缩感知
稀疏信号
信号重构
Bayesian Compressed Sensing
Sparse Signal
Signal Reconstruction
