Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation



Low energy consumption and limited power supply are significant factors for wireless sensor networks (WSNs); thus, distributed state estimation and data fusion with quantized innovation are explored. The universal features of practical WSNs are investigated, and a dynamic transmission strategy is introduced. Furthermore, quantization state estimation based on Bayesian theory is derived. Unlike previous algorithms suitable for processing scalar measurement, the proposed distributed data fusion algorithm is applicable to general vector measurement. Furthermore, the efficiency of the proposed dynamic transmission strategy is analyzed. It is concluded that the proposed algorithm is more efficient than previous methods, and its estimation accuracy comparable to that of the standard Kalman filtering, which is based on analog-amplitude vector measurement.



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  1. 1

    Masazade E, Niu R, Varshney P K. Dynamic bit allocation for object tracking in wireless sensor networks. IEEE Trans Signal Process, 2012, 60: 5048–5063

    MathSciNet  Article  Google Scholar 

  2. 2

    Leng M, Tay W, Quek T, et al. Distributed local linear parameter estimation using Gaussian SPAWN. IEEE Trans Signal Process, 2015, 63: 244–257

    MathSciNet  Article  Google Scholar 

  3. 3

    Braca P, Willett P, LePage K, et al. Bayesian tracking in underwater wireless sensor networks with port-starboard ambiguity. IEEE Trans Signal Process, 2014, 62: 1864–1878

    MathSciNet  Article  Google Scholar 

  4. 4

    Soltani M, Hempel M, Sharif H. Data fusion utilization for optimizing large-scale wireless sensor networks. In: Proceedings of the IEEE International Conference on Communications, Sydney, 2014. 367–372

    Google Scholar 

  5. 5

    Cheng C, Leung H, Maupin P. A delay-aware network structure for wireless sensor networks with in-network data fusion. IEEE Sens J, 2013, 13: 1622–1631

    Article  Google Scholar 

  6. 6

    Kreibich O, Neuzil J, Smid R. Quality-based multiple-sensor fusion in an industrial wireless sensor network for MCM. IEEE Trans Ind Electron, 2014, 61: 4903–4911

    Article  Google Scholar 

  7. 7

    Riberio A, Giannaki G B, Rounmeliotis S I. SOI-KF: distributed Kalman filtering with low-cost communications using the sign of innovations. IEEE Trans Signal Process, 2006, 54: 4782–4795

    Article  Google Scholar 

  8. 8

    Msechu E J, Roumeliotis S I, Ribeiro A, et al. Decentralized quantized Kalman filtering with scalable communication cost. IEEE Trans Signal Process, 2008, 56: 3727–3741

    MathSciNet  Article  Google Scholar 

  9. 9

    Msechu E J, Ribeiro A, Roumeliotis S I, et al. Distributed Kalman filtering based on quantized innovation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, 2008. 3293–3296

    Google Scholar 

  10. 10

    You K, Xie L, Sun S, et al. Multiple-level quantized innovation Kalman filtering. In: Proceedings of the 17th IFAC World Congress, COEX, 2008. 1420–1425

    Google Scholar 

  11. 11

    You K, Xie L, Sun S, et al. Quantized filtering of linear stochastic system. Trans Inst Meas Contr, 2011, 33: 683–689

    Article  Google Scholar 

  12. 12

    Ben-Israel A, Greville T. Generalized Inverses: Theory and Applications. 2nd ed. New York: Springer, 2003

    Google Scholar 

  13. 13

    Bar-Shalom Y, Li X, Kirubarajan T. Estimation with Applications to Tracking and Navigation. New York: Wiley, 2001

    Google Scholar 

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Correspondence to Zhi Zhang.

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Zhang, Z., Li, J. & Liu, L. Distributed state estimation and data fusion in wireless sensor networks using multi-level quantized innovation. Sci. China Inf. Sci. 59, 1–15 (2016). https://doi.org/10.1007/s11432-015-5415-6

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  • data fusion
  • distributed state estimation
  • target tracking
  • Kalman filtering
  • quantization
  • wireless sensor networks


  • 022316


  • 数据融合
  • 分布式状态估计
  • 目标跟踪
  • 卡尔曼滤波
  • 量化
  • 无线传感器网络