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Czasopismo

Archives of Control Sciences

Tytuł artykułu

Fusion Kalman filtration for distributed multisensor systems

Autorzy Duda, Z. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN In the paper, fusion state hierarchical filtration for a multisensor system is considered. An optimal global Kalman filter is realized by a central node in the information form. The state estimate depends on local information that should be sent by local nodes. Two information structures are considered in the paper. In the first case local estimates are based on the local measurement information. It leads to distributed Kalman filter fusion that is well known in a literature. In the second case local node has additionally global information of the system with one step delay. A synthesis of local filters is presented. An advantage of this structure is discussed.
Słowa kluczowe
EN fusion Kalman filtration   multisensor system   one step delay information  
Wydawca Polish Academy of Sciences, Committee of Automation and Robotics
Czasopismo Archives of Control Sciences
Rocznik 2014
Tom Vol. 24, no. 1
Strony 53--65
Opis fizyczny Bibliogr. 13 poz., wzory
Twórcy
autor Duda, Z.
  • Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-101 Gliwice, Poland, zdzislaw.duda@polsl.pl
Bibliografia
[1] C. Y. Chong, S. Mori and K. C. Chang: Distributed multitarget multisensor tracking. In: Multitarget-multisensor tracking: Advanced applications, 1, Norwood, Ma: Atech House, 1990.
[2] K. C. Chang, R. H. Saha and Y. Bar-Shalom: On optimal track to track fusion. IEEE Trans. on Aerospace and Electronic Systems, 33 (1997), 1271-1276.
[3] K. C. Chang, Z. Tian and S. Mori: Performance evaluation for MAP state estimate fusion. IEEE Trans. on Aerospace and Electronic Systems, 40, (2004), 706-714.
[4] H. M. Chen, T. Kirubarajan and Y. Bar-Shalom: Performance limits on track to track fusion versus centralized estimation. IEEE Trans. on Aerospace and Electronic Systems, 39, (2003), 386-400.
[5] Z. Duan and X. R. Li: Lossless linear transformation of sensor data for distributed estimation fusion. IEEE Trans. on Signal Proc., 59 (2011), 362-372.
[6] S. Grim, H. F. Durrant-Whyte and P. Ho: Communication in decentralized data-fusion systems. Proc. American Control Conf., (1992), 3299-3303.
[7] H. Hashmipour, S. Roy and A. Laub: Decentralized structures for parallel Kalman filtering. IEEE Trans. Automatic Control, 33, (1988), 88-93.
[8] H. V. Henderson and S. R. Searle: On deriving the inverse of a sum of matrices.SIAM Rewiev, 23 (1981), 53-60.
[9] B. Khaleghi, A. Khamis, F. O. Karray and S. N. Razavi: Multisensor data fusion: A review of the state-of-theart. Information Fusion, 14 (2013), 28-44.
[10] J. S. Meditch: Stochastic Optimal Linear Estimation and Control. Mc Graw-Hill, Inc., 1965.
[11] J. Sijs, M. Lazar, P. P. J. Den Bosch and Z. Papp: An overview of noncentralized Kalman filters. Proc. of the 17th IEEE Int. Conf. on Control Applications, USA, (2008).
[12] E. B. Song, Y. M. Zhu, J. Zhou and Z. S. You: Optimal Kalman filtering fusion with cross-correlated sensor noises. Automatica, 43 (2007), 1450-1456.
[13] Y. Zhu, Z. You, J. Zhao, K. Zhang and X. R. Li: The optimality for the distributed Kalman filtering fusion with feedback. Automatica, 37 (2001), 1489-1493.
Uwagi
This work has been supported with a grant from the Polish Ministry of Science and High Education.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-e3921962-de33-44c5-8082-4388a1a2e9fa
Identyfikatory
DOI 10.2478/acsc-2014-0004