Wireless sensor networks WSNs signal processing method based on sparse dictionary

A wireless sensor, sparse dictionary technology, applied in electrical components, code conversion, etc., can solve the problems of inability to reconstruct successfully, poor reconstruction performance, etc., to achieve the effect of improving the success rate, high success rate, and ensuring reconstruction accuracy

Inactive Publication Date: 2016-07-06
NANJING UNIV OF POSTS & TELECOMM
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When the WSNs signal is sparsely reconstructed using the DCT basis and wavelet basis, the reconstruction performance is

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  • Wireless sensor networks WSNs signal processing method based on sparse dictionary
  • Wireless sensor networks WSNs signal processing method based on sparse dictionary
  • Wireless sensor networks WSNs signal processing method based on sparse dictionary

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[0029] The technical solution of the invention will be described in detail below in conjunction with the drawings.

[0030] Such as figure 1 As shown, it is a system block diagram of a method for wireless sensor network signal processing based on sparse dictionary K-SVD-DCT proposed by the present invention. Deploy WSNs nodes in a natural environment for distributed collection, and the obtained WSNs signals are used as original signals (1), and 1 / 3~2 / 3 of the original signals are randomly selected as training signals (2); first, in the training phase, use K -The adaptability of the SVD algorithm, through training iterations to update the dictionary, construct a sparse dictionary K-SVD-DCT that is based on the training signal and can obtain a good sparse representation of the original signal on the basis of the training signal; then in the observation stage, According to the observation matrix in the compressed sensing CS theory, the original signal is compressed and sampled; fina...

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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...

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Application Information

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IPC IPC(8): H03M7/30
CPCH03M7/3062
Inventor 邹志强王银霞沈澍吴家皋
Owner NANJING UNIV OF POSTS & TELECOMM
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