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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-53bf9991-f28c-408e-98c4-1fe3afe6eabd

Czasopismo

Archives of Control Sciences

Tytuł artykułu

Fusion Kalman filtration with k-step delay sharing pattern

Autorzy Duda, Z. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN A fusion hierarchical state filtration with k−step delay sharing pattern for a multisensor system is considered. A global state estimate depends on local state estimates determined by local nodes using local information. Local available information consists of local measurements and k−step delay global information - global estimate sent from a central node. Local estimates are transmitted to the central node to be fused. The synthesis of local and global filters is presented. It is shown that a fusion filtration with k−step delay sharing pattern is equivalent to the optimal centralized classical Kalman filtration when local measurements are transmitted to the center node and used to determine a global state estimate. It is proved that the k−step delay sharing pattern can reduce covariances of local state errors.
Słowa kluczowe
EN fusion Kalman filtration   multisensor system   k− step delay sharing pattern  
Wydawca Polish Academy of Sciences, Committee of Automation and Robotics
Czasopismo Archives of Control Sciences
Rocznik 2015
Tom Vol. 25, no. 3
Strony 307--318
Opis fizyczny Bibliogr. 11 poz., wzory
Twórcy
autor Duda, Z.
  • Institute of Automatic Control, Silesian Technical University, ul. Akademicka 16, 44-101 Gliwice, Poland, zdzislaw.duda@polsl.pl
Bibliografia
[1] K. Chang, R. Saha and Y. Bar-Shalom: On optimal track to track fusion. IEEE Trans. on Aerospace and Electronic Systems, 33 (1997), 1271-1276.
[2] K. Chang, Z. Tian and S. Mori: Performance evaluation for MAP state estimate fusion. IEEE Trans. on Aerospace and Electronic Systems, 40 (2004), 706-714.
[3] H. 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.
[4] C. Chong, S. Mori and K. Chang: Distributed multitarget multisensor tracking. In: Multitarget-multisensor tracking: Advanced applications, 1 Norwood, Ma: Atech House, 1990.
[5] Z. Duda: Fusion Kalman filtration for distributed multisensor systems. Archives of Control Sciences, 24 (2014), 53-65.
[6] S. Grim, H. F. Durrant-Whyte and P. Ho: Communication in Decentralized Data-Fusion Systems. In: Proc. American Control Conference, Chicago (1992), 3299-3303.
[7] H. Hashmipour, S. Roy and A. Laub: Decentralized Structures for Parallel Kalman Filtering. IEEE Trans. on Automatic Control, 33 (1988), 88-93.
[8] B. Khaleghi, A. Khamis, F. O. Karray and S. N. Razavi: Multisensor data fusion: A review of the state-of-the art. Information Fusion, 14 (2013), 28-44.
[9] J. S. Meditch: Stochastic optimal linear estimation and control. Mc Graw–Hill, Inc., US 1969.
[10] J. Sijs M. Lazar, P. Bosch and Z. Papp: An overview of non-centralized Kalman filters. In: Proc. of the 17th IEEE Int. Conf. on Control Applications, San Antonio, USA, (2008), 739-744.
[11] 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
EN This work has been supported with a grant from the Polish Ministry of Science and High Education.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-53bf9991-f28c-408e-98c4-1fe3afe6eabd
Identyfikatory
DOI 10.1515/acsc-2015-0020