Skip to main content
SpringerLink
Log in

Energy-Efficient Weighted Observation Fusion Kalman Filtering with Randomly Delayed Measurements

  • Short Paper
  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

This paper is concerned with the weighted observation fusion Kalman filtering problem for a class of multi-sensor fusion systems with random measurement delays and energy constraints. To reduce the energy consumptions, each sensor intermittently sends information to the fusion center in a random way. By using the full rank decomposition approach, the observation fusion equation is derived. Without resorting to the augmentation technique, optimal weighted observation fusion Kalman filter (WOFKF) is given, and it is proved that the performance of the WOFKF is equivalent to that of the centralized fusion Kalman filter. Simulations show the effectiveness of the proposed fusion methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. A.S. Behbahani, A.M. Eltawil, H. Jafarkhani, Linear decentralized estimation of correlated data for power-constrained wireless sensor networks. IEEE Trans. Signal Process. 60, 6003–6016 (2012)

    Article  MathSciNet  Google Scholar 

  2. R.S. Blum, Ordering for estimation and optimization in energy efficient sensor networks. IEEE Trans. Signal Process. 59, 2847–2856 (2011)

    Article  MathSciNet  Google Scholar 

  3. S. Burleigh, A. Hooke, L. Torgerson, K. Fall et al., Delay-tolerant networking: an approach to interplanetary internet. IEEE Commun. Mag. 41, 128–136 (2003)

    Article  Google Scholar 

  4. N.A. Carlson, Federated square root filter for decentralized parallel process. IEEE Trans. Aerosp. Electron. Syst. 26, 517–525 (1990)

    Article  Google Scholar 

  5. B. Chen, L. Yu, W.A. Zhang, H. Song, Networked multi-sensor fusion estimation with delays, packet losses and missing measurements. in ICARCV (Guangzhou, China) (2012) pp. 695–700

  6. B. Chen, W.A. Zhang, L. Yu, Distributed fusion estimation with missing measurements, random transmission delays and packet dropouts. IEEE Trans. Autom. Control (2013). doi:10.1109/TAC.2013.2297192

  7. B. Chen, L. Yu, W.A. Zhang, A. Liu, Robust information fusion estimator for multiple delay-tolerant sensors with different failure rates. IEEE Trans. Circuits Syst. I Reg. Papers 60, 401–414 (2013)

    Article  MathSciNet  Google Scholar 

  8. B. Chen, L. Yu, W.A. Zhang, H. Wang, Distributed \({H_\infty }\) fusion filtering with communication bandwidth constraints. Signal Process. 96, 284–289 (2014)

    Article  Google Scholar 

  9. B. Chen, W.A. Zhang, L. Yu, Distributed finite-horizon fusion Kalman filtering for bandwidth and energy constrained wireless sensor networks. IEEE Trans. Signal Process. 62, 797–812 (2014)

    Article  MathSciNet  Google Scholar 

  10. Z.S. Duan, X.R. Li, M. Yang, Why are more sensors better in estimation fusion? in Proceedings of International Colloquium on Information Fusion (Xi’an, China, 2007), pp. 173–179

  11. Q. Gan, C.J. Harris, Comparison of two measurement fusion methods for Kalman-filter-based multisensory data fusion. IEEE Trans. Aerosp. Electron. Syst. 37, 273–279 (2001)

    Article  Google Scholar 

  12. Y. Gao, C. Ran, Z.L. Deng, Weighted measurement fusion Kalman filter based on linear unbiased minimum variance criterion. in Proceedings of the IEEE Conference Decision and Control (Shanghai, China, 2009), pp. 7593–7598

  13. H. Gao, X. Meng, T. Chen, Stabilization of networked control systems with a new delay characterization. IEEE Trans. Autom. Control. 53, 2142–2148 (2008)

    Article  MathSciNet  Google Scholar 

  14. A. Hmamed, C.E. Kasri, E.H. Tissir, T. Alvarez, F. Tadeo, Robust \({H_\infty }\) filtering for uncertain 2-D continuous systems with delays. Int. J. Innov. Comput. I 9, 2167–2183 (2013)

    Google Scholar 

  15. A.H. Jazwioski, Stochastic Processes and Filtering Theory (Academic, New York, 1970)

    Google Scholar 

  16. X.R. Li, Y.M. Zhu, J. Wang, C.Z. Han, Optimal linear estimation fusion-part I: unified fusion rules. IEEE Trans. Inf. Theory 49, 2192–2208 (2003)

    Article  Google Scholar 

  17. F. Li, X. Zhang, Delay-range-dependent robust \({H_\infty }\) filtering for singular LPV systems with time varying delay. Int. J. Innov. Comput. I 9, 339–353 (2013)

    Google Scholar 

  18. J. Li, G. Alregib, Distributed estimation in energy-constrained wireless sensor networks. IEEE Trans. Signal Process. 57, 3746–3758 (2009)

    Article  MathSciNet  Google Scholar 

  19. N. Lv, S.L. Sun, Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements. in Proceedings of the IEEE Conference Decision and Control (Shanghai, China, 2009), pp. 7599–7602

  20. I. Penarrocha, R. Sanchis, P. Albertos, Estimation in multi-sensor networked systems with scare measurements and time-varying delays. Syst. Contr. Lett. 61, 555–562 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. R. Piziak, P.L. Odell, Full rank factorization of matrices. Math. Mag. 72, 193–201 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  22. J.A. Roecker, C.D. McGillen, Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion. IEEE Trans. Aerosp. Electron. Syst. 21, 447–449 (1998)

    Google Scholar 

  23. L. Schenato, Optimal sensor fusion for distributed sensors subject to random delay and packet loss. in Proceedings of the IEEE Conference Decision and Control (2006), pp. 1547–1552

  24. X. Su, P. Shi, L. Wu, S.K. Nguang, Induced \(l_2\) filtering of fuzzy stochastic systems with time-varying delays. IEEE Trans. Cybern. 43, 1251–1264 (2013)

    Article  Google Scholar 

  25. X. Su, L. Wu, P. Shi, Sensor networks with random link failures: distributed filtering for T–S fuzzy systems. IEEE Trans. Indus. Inform. 9, 739–1750 (2013)

    Article  Google Scholar 

  26. S.L. Sun, Z.L. Deng, Multi-sensor optimal information fusion Kalman filter. Automatica 40, 1017–1023 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  27. S.L. Sun, W. Xiao, Distributed weighted fusion estimators with random delays and packet dropping. Circuits Syst. Signal. Process 26, 591–605 (2007)

    Article  MATH  Google Scholar 

  28. Y. Wang, H. Wu, Delay/fault-tolerant mobile sensor networks (DFT-MSN): a new paradigm for pervasive information gathering. IEEE Trans. Mobile Comput. 6, 1021–1034 (2007)

    Article  Google Scholar 

  29. Z. Wang, Y. Niu, Distributed estimation and filtering for sensor networks. Int. J. Syst. Sci. 12, 1421–1425 (2011)

    Article  MathSciNet  Google Scholar 

  30. Y. Xia, J. Shang, J. Chen, G.P. Liu, Networked data fusion with packet losses and variable delays. IEEE Trans. Syst. Man Cybern. B Cybern. 39, 1107–1120 (2009)

    Article  Google Scholar 

  31. J.J. Xiao, S. Cui, Z. Luo, A.J. Goldsmith, Power scheduling of universal decentralized estimation in sensor networks. IEEE Trans. Signal Process. 54, 413–422 (2006)

    Article  Google Scholar 

  32. R. Yang, P. Shi, G.P. Liu, Filtering for discrete-time networked nonlinear systems with mixed random delays and packet dropouts. IEEE Trans. Autom. Control 56, 2655–2660 (2011)

    Article  MathSciNet  Google Scholar 

  33. W.A. Zhang, L. Yu, X. Qiu, B. Chen, Energy-efficient fusion estimation for wireless sensor networks with packet losses. in 30th CCC (China, Yantai, 2011), pp. 6408–6412

  34. H. Zhu, I.D. Schizas, G.B. Giannakis, Power-efficient dimensionality reduction for distributed channel-aware Kalman tracking using WSNs. IEEE Trans. Signal Process. 57, 3193–3207 (2009)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61304256), Zhejiang Provincial Natural Science Foundation of China (LQ13F030013), and Ningbo Natural Science Foundation (2012A610016). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers and the associate editor as well as that of the Editor-in-Chief, which have improved the presentation.

Author information

Authors and Affiliations

  1. Digital Information Technology Department, Zhejiang Technical Institute of Economics, Hangzhou , 310018, Zhejiang, People’s Republic of China

    Jian Chen

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou , 310023, Zhejiang, People’s Republic of China

    Bo Chen

  3. Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou , 310018, Zhejiang, People’s Republic of China

    Zhen Hong

Authors
  1. Jian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Chen, B. & Hong, Z. Energy-Efficient Weighted Observation Fusion Kalman Filtering with Randomly Delayed Measurements. Circuits Syst Signal Process 33, 3299–3316 (2014). https://doi.org/10.1007/s00034-014-9790-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-014-9790-9

Keywords

Search

Navigation