›› 2014, Vol. 23 ›› Issue (7): 70504-070504.doi: 10.1088/1674-1056/23/7/070504

• GENERAL • 上一篇    下一篇

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

黄锦旺, 冯久超   

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
  • 收稿日期:2013-08-15 修回日期:2014-02-07 出版日期:2014-07-15 发布日期:2014-07-15
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 60872123), the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation, China (Grant No. U0835001), the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No. 2012ZM0025), the South China University of Technology, China, and the Fund for Higher-level Talent in Guangdong Province, China (Grant No. N9101070).

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

Huang Jin-Wang (黄锦旺), Feng Jiu-Chao (冯久超)   

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
  • Received:2013-08-15 Revised:2014-02-07 Online:2014-07-15 Published:2014-07-15
  • Contact: Feng Jiu-Chao E-mail:fengjc@scut.edu.cn
  • About author:05.45.-a; 05.45.Vx; 84.40.Ua
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 60872123), the Joint Fund of the National Natural Science Foundation and the Guangdong Provincial Natural Science Foundation, China (Grant No. U0835001), the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No. 2012ZM0025), the South China University of Technology, China, and the Fund for Higher-level Talent in Guangdong Province, China (Grant No. N9101070).

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摘要： For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

关键词: cubature rule, particle filter, signal reconstruction, chaotic signals

Abstract: For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.

Key words: cubature rule, particle filter, signal reconstruction, chaotic signals

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
05.45.Vx (Communication using chaos) 84.40.Ua (Telecommunications: signal transmission and processing; communication satellites)