期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2014
卷号：7
期号：1
页码：225-232
DOI：10.14257/ijhit.2014.7.1.18
出版社：SERSC
摘要：Compressive sensing (CS) is a novel framework which exploits both the sparsity and the intra-correlation of the signal in structural health monitoring (SHM) based on wireless sensor networks (WSNs). It contains sparse signal representation, the measurement matrix selection and the reconstruction algorithm. The SHM signal is recovered by M measurements following the restricted isometry constant (RIC). However, the signal should be denoised before reconstruction. This paper discusses two wavelet noise reduction methods, soft threshold and hard threshold method, and verifies the performance of different methods for SHM signal reconstruction. Experimental results show that wavelet hard threshold method has much better effect on SHM sparse signal reconstruction than soft threshold method. Meanwhile, we can get a more accurate corresponding relation of RIC that is *log( / ) 33 M CK N K
