Wireless sensor networks WSNs signal processing method based on sparse dictionary

Abstract

The invention discloses a wireless sensor networks WSNs signal processing method based on a sparse dictionary. Firstly, in a training stage, the sparse dictionary of WSNs signals is constructed through comprehensive adoption of a K-SVD (K-Means Singular Value Decomposition) algorithm and a DCT (Discrete Cosine Transform) initialization dictionary, thus obtaining sparse representation of the WSNs signals; then in a sampling stage, a Gauss random matrix is taken as an observing matrix to carry out CS (compressed sensing) sampling to the WSNs; and finally, based on the sparse dictionary K-SVD-DCT and the Gauss random observing matrix, approximate original WSNs signals are reconstructed through adoption of an l1 norm minimization method. The sparse dictionary K-SVD-DCT constructed in the method has good self-adaptability; the good sparse representation of the WSNs signals can be realized in the sparse dictionary K-SVD-DCT; and based on the sparse dictionary K-SVD-DCT, the WSNs signal compressed sensing reconstruction success rate is high, and the reconstruction precision is good.

Description

technical field [0001] The invention belongs to the field of wireless sensor network (WSNs) signal sampling and signal processing, in particular to a method for signal processing of wireless sensor network (WSNs) based on sparse dictionary (K-SVD-DCT). Background technique [0002] With the rapid development of computer networks, wireless sensor networks (WSNs) have been widely used in many modern technology fields due to their low power consumption and low cost. Moreover, since a large number of sensor nodes in WSNs can directly replace manpower to complete specific monitoring tasks, this not only reduces the cost of manpower, but also avoids the danger of many areas that need to be monitored or the harsh environment that may cause harm to the human body. damage, which makes WSNs have good research value. The sensor node is usually a miniature embedded system, its perception processing and communication capabilities are relatively weak, and its battery energy is limited, a...

AI Technical Summary

Problems solved by technology

When the WSNs signal is sparsely reconstructed using the DCT basis and wavelet basis, the reconstruction performance is relatively poor under the generally set reconstruction function threshold, and the reconstruction is often unsuccessful.

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