State estimation for asynchronous multirate multisensor dynamic systems with missing measurements

State estimation for asynchronous multirate multisensor dynamic systems with missing measurements

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study is concerned with the state estimation problem for a kind of asynchronous multirate multisensor dynamic system, where observations from different sensors are randomly missing. The system is described at the highest sampling rate with different sensors observing a single target independently with multiple sampling rates. The optimal state estimate is obtained by use of the multiscale system theory and the modified Kalman filter. This study extends the federated Kalman filter to the case of asynchronous multirate multisensor dynamic systems with measurements randomly missing. The presented algorithm is proven to be effective in the sense of linear minimum mean squared error. The feasibility and efficiency of the algorithm are illustrated by a numerical simulation example.


    1. 1)
      • Estimation with application to tracking and navigation
    2. 2)
      • Federated square root filter for decentralized parallel processors
    3. 3)
      • On optimal track-to-track fusion
    4. 4)
      • Architectures and algorithms for track association and fusion
    5. 5)
      • Multisensor data fusion
    6. 6)
      • Theory of distributed estimation using multiple asynchronous sensors
    7. 7)
      • Update with out-of-sequence-measurements in tracking: exact solution
    8. 8)
      • Multiresolutional filtering using wavelet transform
    9. 9)
      • Modeling and estimation of multiresolution stochastic processes
    10. 10)
      • Benveniste, A., Nikoukhah, R., Willsky, A.S.: `Multiscale system theory', Proc. 29th IEEE Conf. on Decision and Control, 1990, Honolulu, Hawaii, 4, p. 2484–2487
    11. 11)
      • Multiscale estimation theory and application
    12. 12)
      • An asynchronous multirate multisensor information fusion algorithm
    13. 13)
      • Multiresolution modeling and estimation of multisensor data
    14. 14)
      • The modeling and estimation of asynchronous multirate multisensor dynamic systems
    15. 15)
      • Filterbanks design for multisensor data fusion
    16. 16)
      • Variance-constrained filtering for uncertain stochastic systems with missing measurements
    17. 17)
      • Kalman filtering with intermittent observations
    18. 18)
      • Boers, Y., Driessen, H., Zwaga, J.: `The modified riccati equation in target tracking: some recent results', Proc. 2005 Int. Conf. on Information Fusion, 25–29 July 2005, Philadelphia, USA
    19. 19)
      • Kalman filtering with faded measurements
    20. 20)
      • Stability of Kalman filtering with Marikovian packet losses
    21. 21)
      • Networked data fusion with packet losses and variable delays
    22. 22)
      • Multi-rate optimal state estimation
    23. 23)
      • Liu, B.S., Yan, L.P., Shi, H.: `State fusion estimation with missing measurements', Proc. Int. Conf. 2007 on Information Computing and Automation (ICICA'07), 2007, II, p. p. IP-2-97
    24. 24)
      • Kalman filtering: with real-time applications
    25. 25)
      • Multi-sensor optimal information fusion Kalman filter

Related content

This is a required field
Please enter a valid email address