摘要
随着无线传感器网络的快速发展,海量数据的处理、存储与传输给传统的以高速ADC和存储通信设备带来了巨大的压力。由于传感器节点采集的感知数据具有时间相关性,本文提出基于压缩感知理论的采样压缩方法,其打破了传统奈奎斯特采样定理的限制,在前端只需远低于奈奎斯特采样频率采样信号就可以完成对原始信号的精确重构,并构造了基于压缩感知的模拟信息转换器(AIC)模型。最后通过以Matlab为平台进行实验仿真,结果表明:该模型可以用较少的观测值即可精确重构稀疏信号,并且其重构精度与观测数M、稀疏度K有关。
With the rapid development of wireless sensor networks（ WSNs）,processing,storing and transmitting huge amounts of data for traditional ADC at high speed and storage communication equipment has brought great pressure. Due to the time of sensory data from the sensor node has a correlation,put forward sampling and compression method based on compressed sensing theory,it breaks the limitation of traditional Nyquist sampling theorem,in the front just is far lower than the Nyquist sampling frequency sampling signal can complete the accurate reconstruction of the original signal,and construct model analog-to-information convertor（ AIC）,based on compression perception using platform of Matlab simulation experiment is carried out,the results show that the model can accurately reconstruct sparse signal using less observations,and the reconstruction precision is related to the observed number M and the sparse degree K.
出处
《传感器与微系统》
CSCD
2016年第8期141-143,147,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61350008)
江苏省高校产业化推进项目(JHB2012-55)
关键词
压缩感知
模拟信息转换器
稀疏信号
compressed sensing（CS）
analog-to-information convertor（AIC）
sparse signal
