摘要： 为提高无线传感器网络的数据恢复效果,提出一种新的数据恢复算法。利用联合稀疏分解的方法,融合多种参数信号,提取出共同分量,使用信号分块方法,减少选择初始稀疏度与步长所带来的影响。实验结果表明,与分段正交匹配追踪算法、稀疏度自适应匹配追踪算法等相比,该算法能降低误差,提高重构信号的精度,并有效缩短运算时间。

关键词: 数据恢复, 多属性, 压缩感知, 无线传感器网络, 数据丢失

Abstract: In order to improve the effect of data recovery after data loss in Wireless Sensor Network(WSN),a new data recovery algorithm is proposed.The joint sparse decomposition method is used to fuse multiple parameters and extract common components.The influence of initial sparsity and step size on selection is also reduced by using signal block method.Experimental results show that compared with Stagewise Orthogonal Macthing Pursuit(StOMP) algorith and Sparsity Adaptive Matching Pursuit(SAMP) algorithm and so on,the algorithm can reduce the error caused by restoration,improve the accuracy of reconstructed signal,and shorten the time needed for computation effectively.

Key words: data recovery, multiattribute, compression sensing, Wireless Sensor Network(WSN), data loss

中图分类号: 

• TP391