Kalman Fusion Estimation for WSNs with Nonuniform Estimation Rates

  • Wen-An Zhang
  • Bo Chen
  • Haiyu Song
  • Li Yu


As mentioned in Chap.  2, developing energy-efficient algorithms for WSN-based estimation is of great practical significance since the sensor nodes are usually constrained in energy. As usually did in WSNs, one may purposively close the sensor nodes to save power during certain time interval and wake them up when necessary. That is to say, in many situations, it is not necessary for sensors to transmit measurements and generate estimates at every sampling instant from the energy-efficiency perspective, and the sensors may work and generate estimates with two rates, namely, a fast rate and a slow rate according to their power situations. Therefore, adopting a nonuniform estimation rate is a more preferable strategy for sensor network-based estimation system with energy constraints.


Sensor Node Local Estimate Fusion Rule Fusion Algorithm Sampling Instant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan MI, Sastry SS (2001) Kalman filtering with intermittent observations. IEEE Trans Autom Control 49(9):1453–1464MathSciNetCrossRefGoogle Scholar
  2. 2.
    Huang M, Dey S (2007) Stability of Kalman filtering with Markovian packet losses. Automatica 43(4):598–607MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Zhang WA, Feng G, Yu L (2011) Optimal linear estimation for networked systems with communication constraints. Automatica 47(9):1992–2000MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Dong H, Wang Z, Gao H (2012) Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans Signal Process 60(6):3164–3173MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hounkpevi FO, Yaz EE (2007) Robust minimum variance linear state estimators for multiple sensors with different failure rates. Automatica 43(7):1274–1280MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Shen B, Wang ZD, Liu XH (2011) A stochastic sampled-data approach to distributed \(H_{\infty }\) filtering in sensor networks. IEEE Trans Circuit Syst-I Regul Pap 58(9):2237–2246MathSciNetCrossRefGoogle Scholar
  7. 7.
    Carlson NA (1990) Federated square root filter for decentralized parallel processes. IEEE Trans Aerosp Electron Syst 26(3):517–529CrossRefGoogle Scholar
  8. 8.
    Kim KH (1994) Development of track to track fusion algorithm. In: Proceedings of the American control conference, Baltimore, pp 1037–1041Google Scholar
  9. 9.
    Sun SL, Deng ZL (2004) Multi-sensor optimal information fusion Kalman filter. Automatica 40(6):1017–1023MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Bar-Shalom Y, Li XR (1990) Multitarget-multisensor tracking: advanced applications, vol 1. Artech House, NorwoodGoogle Scholar
  11. 11.
    Chong CY, Chang KC, Mori S (1986) Distributed tracking in distributed sensor networks. In: Proceedings of the 1986 American control conference, Seattle, pp 1863–1868Google Scholar
  12. 12.
    Chong CY, Mori S, Chang KC (1990) Distributed multitarget multisensor tracking. In: Bar-Shalom Y (ed) Multitarget-multisensor tracking: advanced applications, vol 1. Artech House, NorwoodGoogle Scholar
  13. 13.
    Cristi R, Tummala M (2000) Multirate, multiresolution, recursive Kalman filter. Signal Process 80(9):1945–1958CrossRefzbMATHGoogle Scholar
  14. 14.
    Fabrizio A, Luciano A (2000) Filterbanks design for multisensor data fusion. IEEE Signal Process Lett 7(5):100–103CrossRefGoogle Scholar
  15. 15.
    Hong L (1992) Distributed filtering using set models. IEEE Trans Aerosp Electron Syst 27(4):715–724CrossRefGoogle Scholar
  16. 16.
    Yan LP, Liu BS, Zhou DH (2007) Asynchronous multirate multisensor information fusion algorithm. IEEE Trans Aerosp Electron Syst 43(3):1135–1146MathSciNetCrossRefGoogle Scholar
  17. 17.
    Alouani AT, Gray JE, McCabe DH (2005) Theory of distributed estimation using multiple asynchroous sensors. IEEE Trans Aerosp Electron Syst 41(2):717–722CrossRefGoogle Scholar
  18. 18.
    Hu YY, Duan ZS, Zhou DH (2010) Estimation fusion with general asynchronous multi-rate sensors. IEEE Trans Aerosp Electron Syst 46(4):2090–2102CrossRefGoogle Scholar
  19. 19.
    Wang F, Balakrishnan V (2002) Robust Kalman filters for linear time-varying systems with stochastic parametric uncertainties. IEEE Trans Signal Process 50(4):803–813MathSciNetCrossRefGoogle Scholar
  20. 20.
    Anderson BDO, Moore JB (1979) Optimal filtering. Prentice-Hall, Englewood CliffszbMATHGoogle Scholar
  21. 21.
    Liang Y, Chen TW, Pan Q (2009) Multi-rate optimal state estimation. Int J Control 82(11):2059–2076MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Science Press, Beijing and Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Wen-An Zhang
    • 1
  • Bo Chen
    • 1
  • Haiyu Song
    • 2
  • Li Yu
    • 3
  1. 1.Department of AutomationZhejiang University of TechnologyHangzhouChina
  2. 2.Zhejiang Uni. of Finance & EconomicsHangzhouChina
  3. 3.Zhejiang University of TechnologyHangzhouChina

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