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Tytuł artykułu

State estimation in a decentralized discrete time LQG control for a multisensor system

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Języki publikacji
EN
Abstrakty
EN
In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes. In the paper, contrary to this, the controls are not transmitted between nodes. It leads to nonconventional filtration because the controls from other nodes are treated as random variables for each node. The cost for the additional reduced transmission is an increased filter computation at each node.
Słowa kluczowe
Rocznik
Strony
29--39
Opis fizyczny
Bibliogr. 12 poz., wzory
Twórcy
autor
  • Silesian University of Technology, Gliwice, Poland
Bibliografia
  • [1] K. C. Chang, R. H. Saha and Y. Bar-Shalom: On optimal track to track fusion. IEEE Trans. on Aerospace and Electronic Systems, 33(4), (1997), 1271-1276.
  • [2] K. C. Chang, Z. Tian and S. Mori: Performance evaluation for MAP state estimate fusion. IEEE Trans. on Aerospace and Electronic Systems, 40(2), (2004), 706-714.
  • [3] Chen H. M., T. Kirubarajan and Y. Bar-Shalom: Performance limits on track to track fusion versus centralized estimation, IEEE Trans. on Aerospace and Electronic Systems, 39(2), pp. 386-400, (2003).
  • [4] Z. Duan and X. R. Li: Lossless Linear transformation of sensor data for distributed estimation fusion. IEEE Trans. on Signal Processing, 59(1), (2011), 362-372.
  • [5] H. Hashmipour, S. Roy and A. Laub: Decentralized structures for parallel Kalman filtering. IEEE Trans. on Automatic Control, 33(1), (1988), 88-93.
  • [6] M. E. Liggins, C. Y. Chong, I. Kadar, M. Alford, V. Vannicola and S. Thomopoulos: Distributed fusion architectures and algorithms for target tracking, Proc. of the IEEE, 85(1), (1997), 95-107.
  • [7] A. G. O. Mutambara and H. F. Durrant-Whyte: Estimation and control for a modular wheeled mobile robot. IEEE Trans. on Control Systems Technology, 8(1), (2000) 35-46.
  • [8] M. S. Schlosser and K. Kroschel: Performance analysis of decentralized Kalman filters under communication constraints. J. of Advances in Information Fusion, 2(2), (2007), 65-75.
  • [9] 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).
  • [10] 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.
  • [11] J. L. Speyer: Computation and transmission requirements for a decentralized Linear-Quadratic-Gaussian control problem. IEEE Trans. on Automatoc Control, 24(2), (1979), 266-269.
  • [12] Y. Zhu, Z. You, J. Zhao, K. Zhang and X. R. Li: The optimality for the distributed Kalman filtering fusion with feedback. Automatica, 37(2), (2001), 1489-1493.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d72dddd4-9c07-47d3-af48-35eb169bd26c
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