摘要
针对结构监测中的结构损伤信号的处理,提出一种基于压缩感知的数据融合方法,实现压缩采样后的稀疏信号的融合和重构。对航空铝板的损伤信号采用高斯随机矩阵将高维信号序列投影到低维空间,获得稀疏采样的线性测量值,实现信号的压缩采样;再对多传感器的线性测量值进行数据融合;最后通过重构算法来实现信号的重构。实验表明,与现有的方法相比,感知融合的方法具有更好的融合性能和抗噪性,能获得更高的数据压缩效果,节省了网络的带宽和能量,更适合于结构损伤信号的处理。
To meet the needs of data compression and data fusion in structural health monitoring（SHM）based on wireless sensor networks（WSNs）,a novel method of multi-sensor data fusion based on compressed sensing（CS）is proposed for wireless structural damage signal to realize data fusion and reconstruction for sparse signals.The damage signals of aviation aluminum plate are measured and projected on to the linear measurement data through inner products with random Gaussian matrix.Measurement data are fused by the Bayesian algorithm.Finally,the damage signals can be reconstructed by the CS method.The experiment results show that compared with the existing method,the proposed method can save the network bandwidth as well as energy,thanks to its good data fusion performance,anti-noise property and better data compression effect.
出处
《数据采集与处理》
CSCD
北大核心
2015年第4期857-867,共11页
Journal of Data Acquisition and Processing
基金
国家行业专项(GYHY201206033)资助项目
国家自然科学基金(51305211)资助项目
江苏省高校自然科学研究计划(11KJB520011)资助项目
关键词
无线传感器网络
结构健康监测
损伤识别
压缩感知
稀疏表示
数据融合
wireless sensor networks（WSNs）
structural health monitoring（SHM）
damage identification
compressed sensing（CS）
sparse representation
data fusion
