关键词: 代价参考/
混沌信号重构/
能耗/
广义似然比
English Abstract
Reconstruction algorithm of chaotic signal based on generalized likelihood ratio threshold-decision
Ren Zi-Liang1,2,Qin Yong2,
Huang Jin-Wang2,
Zhao Zhi1,
Feng Jiu-Chao1
1.School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China;
2.School of Computer Science and Network Security, Dongguan University of Technology, Dongguan 523808, China
Fund Project:Project supported by the National Natural Science Foundation of China (Grant No.60872123),the Joint Fund of the National Natural Science Foundation and the Natural Science Foundation of Guangdong Province,China (Grant No.U0835001),and the Guangdong Higher School Scientific Innovation Project,China (Grant No.2013KJCX0178).Received Date:07 September 2016
Accepted Date:20 November 2016
Published Online:05 February 2017
Abstract:Blind signal reconstruction in sensor arrays is usually a highly nonlinear and non-Gaussian problem, and nonlinear filtering is an effective way to realize state estimation from available observations. Developing the processing problem of blind signal in wireless sensor networks (WSNs) will greatly extend the application scope. Meanwhile, it also meets great challenges such as energy and bandwidth constrained. For solving the constrained problem in WSNs, the observed signals must be quantified before sending to the fusion center, which makes the overall noise unable to be modeled accurately by simple probabilistic model.To study the reconstruction issue of chaotic signal with unknown statistics in WSNs, a reconstructed method of chaotic signal based on a cost reference particle filter (CRPF) is proposed in this paper. The cost recerence cubature particle filter (CRCPF) algorithm adopts cubature-point transformation to enhance the accuracy of prediction particles, and cost-risk functions are defined to complete particle propagation. The effectiveness of proposed CRCPF algorithm is verified in the sensor network with a fusion center. Moreover, a generalized likelihood ratio functionis obtained by the cost increment of local reconstructed signals in the cluster-based sensor network topology model, which is used to reduce the network energy consumption by selecting working nodes. Simulation results show that compared with CPF and CRPF, the proposed algorithm CRCPF attains good performance in a WSN with unknown noise statistics. Meanwhile, the CRCPF algorithm realizes the compromise between energy consumption and reconstruction accuracy simultaneously, which indicates that the proposed CRCPF algorithm has the potential to extend other application scope.
Keywords: cost reference/
chaotic signal reconstruction/
energy consumption/
generalized likelihood ratio
