Skip to main content

Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises

  • Conference paper
  • First Online:
Knowledge Engineering and Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 214))

Abstract

This paper is concerned with the optimal state estimation problem under linear dynamic systems when the sampling rates of different sensors are different. The noises of different sensors are cross-correlated and coupled with the system noise of the previous step. By use of the projection theory and induction hypothesis repeatedly, a sequential fusion estimation algorithm is derived. The algorithm is proven to be optimal in the sense of Linear Minimum Mean Square Error(LMMSE). Finally, a numerical example is presented to illustrate the effectiveness of the proposed algorithm.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Li XR, Zhu YM, Wang J, Han C (2003) Optimal linear estimation fusion-Part 1:unified fusion rules. IEEE Trans Inf Theory 49:2192–2208

    Article  Google Scholar 

  2. Bar-Shalom Y (1990) Multitarget-multisensor tracking: advanced applications, vol 1. Artech House, Norwood

    Google Scholar 

  3. Chong CY, Chang KC, Mori S (1986) Distributed tracking in distributed sensor networks. In: 1986 American control conference, Seattle, WA, pp 1863–1868

    Google Scholar 

  4. Hashemipour HR, Roy S, Laub AJ (1988) Decentralized structures for parallel Kalman filtering. IEEE Trans Autom Control 3(1):88–93

    Article  Google Scholar 

  5. Song E, Zhu Y, Zhou J, You Z (2007) Optimal Kalman filtering fusion with cross-correlated sensor noises. Automatica 43:1450–1456

    Article  MathSciNet  MATH  Google Scholar 

  6. Duan Z, Li XR (2008) The optimality of a class of distributed estimation fusion algorithm. IEEE Inf Fusion 16:1–6

    Google Scholar 

  7. Xiao CY, Ma J, Sun SL (2011) Design of information fusion filter for a class of multi-sensor asynchronous sampling systems. In: Control and decision conference, pp 1081–1084

    Google Scholar 

  8. Yan LP, Zhou DH, Fu MY, Xia YQ (2010) State estimation for asynchronous multirate multisensor dynamic systems with missing measurements. IET Signal Process 4(6):728–739

    Article  MathSciNet  Google Scholar 

  9. Shi H, Yan L, Liu B, Zhu J (2008) A sequential asynchronous multirate multisensor data fusion algorithm for state estimation. Chin J Electron 17:630–632

    Google Scholar 

Download references

Acknowledgments

The corresponding author of this article is Liping Yan, whose work was supported by the NSFC under grants 61004139 and 91120003, the Scientific research base support, and the outstanding youth foundation of Beijing Institute of Technology. The work of Yuanqing Xia and Mengyin Fu was supported by the NSFC under grants 60974011 and 60904086, respectively. The work of Bo Xiao was supported by Beijing Natural Science Foundation under Grant 4123102, and the innovation youth foundation of Beijing University of Posts and Telecommunications.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yulei Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, Y., Yan, L., Xiao, B., Xia, Y., Fu, M. (2014). Multirate Multisensor Data Fusion Algorithm for State Estimation with Cross-Correlated Noises. In: Sun, F., Li, T., Li, H. (eds) Knowledge Engineering and Management. Advances in Intelligent Systems and Computing, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37832-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37832-4_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37831-7

  • Online ISBN: 978-3-642-37832-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics