iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/s00034-021-01707-8
Distributed Dimensionality Reduction Fusion Kalman Filtering With Quantized Innovations | Circuits, Systems, and Signal Processing Skip to main content
Springer Nature Link
Log in

Distributed Dimensionality Reduction Fusion Kalman Filtering With Quantized Innovations

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

Abstract

This paper is concerned with the distributed fusion Kalman filtering problem for networked systems with communication constraints. A dimensionality reduction strategy and a uniform quantization strategy are introduced to reduce communication traffic. To overcome the unboundedness of estimates/measurements in unstable systems, it is proposed to quantize the innovations that are sent to the fusion center through limited bandwidth channels. Then, a recursively distributed dimensionality reduction fusion Kalman filtering algorithm is developed by using a model uncertainty method to process quantization noises. Finally, a target tracking system is employed to demonstrate the effectiveness of the proposed methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data Availability Statement

All data included in this study are available upon request by contacting the corresponding author.

References

  1. Y. Bar-Shalom, T. Kirubarajan, X. Li, Estimation with Applications to Tracking and Navigation (Wilely, New York, 2001).

    Book  Google Scholar 

  2. J. Chen, B. Chen, Z. Hong, Energy-efficient weighted observation fusion Kalman filtering with randomly delayed measurements. Circuits Syst. Signal Process. 33, 3299–3316 (2014)

    Article  MathSciNet  Google Scholar 

  3. P. Chen, S. Cheng, K. Chen, Information fusion to defend intentional attack in Internet of Things. IEEE Internet Things J. 1, 337–348 (2014)

    Article  Google Scholar 

  4. B. Chen, D.W.C. Ho, W.A. Zhang, L. Yu, Distributed dimensionality reduction fusion estimation for cyber-physical systems under DoS attacks. IEEE Trans. Syst. Man Cybern. Syst. 49, 455–468 (2019)

    Article  Google Scholar 

  5. B. Chen, D.W.C. Ho, G. Hu, L. Yu, Secure fusion estimation for bandwidth constrained cyber-physical systems under replay attacks. IEEE Trans. Cybern. 48, 1862–1876 (2018)

    Article  Google Scholar 

  6. B. Chen, D.W.C. Ho, W.A. Zhang, L. Yu, Networked fusion estimation with bounded noises. IEEE Trans. Autom. Control 62, 5415–5421 (2017)

    Article  MathSciNet  Google Scholar 

  7. B. Chen, G. Hu, W.A. Zhang, L. Yu, Distributed fusion estimation with communication bandwidth constraints. IEEE Trans. Autom. Control 60, 1398–1403 (2015)

    Article  MathSciNet  Google Scholar 

  8. B. Chen, G. Hu, W.A. Zhang, L. Yu, Distributed mixed \(H_{2}/H_{\infty }\) fusion estimation with limited communication capacity. IEEE Trans. Autom. Control 61, 805–810 (2016). https://doi.org/10.1109/TAC.2015.2450271

    Article  MATH  Google Scholar 

  9. B. Chen, L. Yu, W.A. Zhang, \(H_{\infty }\) filtering for Markovian switching genetic regulatory networks with time-delays and stochastic disturbances. Circuits Syst. Signal Process. 30, 1231–1252 (2011)

    Article  MathSciNet  Google Scholar 

  10. 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 

  11. R.J. Elliott, J. van der Hoek, Optimal linear estimation and data fusion. IEEE Trans. Autom. Control 51, 686–689 (2006)

    Article  MathSciNet  Google Scholar 

  12. J. Fang, H. Li, Hyperplane-based vector quantization for distributed estimation in wireless sensor networks. IEEE Trans. Inf. Theory 55, 5682–5699 (2009)

    Article  MathSciNet  Google Scholar 

  13. H. Geng, Z. Wang, Y. Cheng, Distributed federated Tobit Kalman filter fusion over a packet-delaying network: a probabilistic perspective. IEEE Trans. Signal Process. 66, 4477–4489 (2018)

    Article  MathSciNet  Google Scholar 

  14. H. Geng, Y. Liang, Y. Liu, F.E. Alsaadi, Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs. Inf. Fusion 39, 139–153 (2017)

    Article  Google Scholar 

  15. U.A. Khan, J.M.F. Moura, Distributing the Kalman filter for large-scale systems. IEEE Trans. Signal Process. 56, 4919–4935 (2008)

    Article  MathSciNet  Google Scholar 

  16. D. Li, S. Kar, F.E. Alsaadi, A.M. Dobaie, S. Cui, Distributed Kalman filtering with quantized sensing state. IEEE Trans. Signal Process. 63, 5180–5193 (2015). https://doi.org/10.1109/TSP.2015.2450200

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  18. W. Liu, X. Wang, Z. Deng, Robust centralized and weighted measurement fusion Kalman predictors with multiplicative noises, uncertain noise variances, and missing measurements. Circuits Syst. Signal Process. 37, 770–809 (2018)

    Article  MathSciNet  Google Scholar 

  19. H. Ma, Y. Yang, Y. Chen, K. Liu, Q. Wang, Distributed state estimation with dimension reduction preprocessing. IEEE Trans. Signal Process. 62, 3098–3110 (2014)

    Article  MathSciNet  Google Scholar 

  20. Y. Pu, M.N. Zeilinger, C.N. Jones, Quantization design for unconstrained distributed optimization, in 2015 American Control Conference (ACC), pp. 1229–1234 (2015). https://doi.org/10.1109/ACC.2015.7170901

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  23. S.P. Talebi, S. Werner, Distributed Kalman filtering and control through embedded average consensus information fusion. IEEE Trans. Autom. Control 64, 4396–4403 (2019)

    Article  MathSciNet  Google Scholar 

  24. S.P. Talebi, S. Werner, D.P. Mandic, Quaternion-valued distributed filtering and control. IEEE Trans. Autom. Control 65, 4246–4257 (2020)

    Article  MathSciNet  Google Scholar 

  25. Y. Wu, Y. Li, B. Chen, Distributed SINR fusion estimation for a class of wireless networks. IEEE Trans. Circuits Syst. II Express Br. 65, 1264–1268 (2018)

    Article  Google Scholar 

  26. B. Xiang, B. Chen, L. Yu, Distributed fusion estimation for unstable systems with quantized innovations. IEEE Trans. Syst. Man Cybern. Syst. (2020). https://doi.org/10.1109/TSMC.2019.2962208

    Article  Google Scholar 

  27. L. Xie, Y.C. Soh, C.E. de Souza, Robust Kalman filtering for uncertain discrete-time systems. IEEE Trans. Autom. Control 39, 1310–1314 (1994)

    Article  MathSciNet  Google Scholar 

  28. Z. Xing, Y. Xia, L. Yan, K. Lu, Q. Gong, Multisensor distributed weighted Kalman filter fusion with network delays, stochastic uncertainties, autocorrelated, and cross-correlated noises. IEEE Trans. Syst. Man Cybern. Syst. 48, 716–726 (2018)

    Article  Google Scholar 

  29. H. Yan, P. Li, H. Zhang, X. Zhan, F. Yang, Event-triggered distributed fusion estimation of networked multisensor systems with limited information. IEEE Trans. Syst. Man Cybern. Syst. (2018). https://doi.org/10.1109/TSMC.2018.2874804

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Xinhao Yan & Xiang Qiu

  2. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Bo Chen

Authors
  1. Xinhao Yan
    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. Xiang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Qiu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported in part by the National Natural Science Funds of China under Grant 61973277 and Grant 62073292 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR20F030004.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, X., Chen, B. & Qiu, X. Distributed Dimensionality Reduction Fusion Kalman Filtering With Quantized Innovations. Circuits Syst Signal Process 40, 5234–5247 (2021). https://doi.org/10.1007/s00034-021-01707-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01707-8

Keywords

Search

Navigation