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Fusion filtration in LQG control for multisensor systems

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Języki publikacji
EN
Abstrakty
EN
In the paper, state filtration in a LQG problem formulated for a multisensor system is considered. Control is determined by a central node as a linear form of a state estimate. It is assumed that control values are not available to local nodes. Because of the drawbacks of centralized filtration an optimal fusion of decentralized local Kalman filters is proposed. When control values are not available to local nodes, then control should be treated as a random variable in the synthesis of local state estimates. This leads to a non-classical estimation. It is shown that the proposed filter is equivalent to the centralized one.
Rocznik
Strony
743--754
Opis fizyczny
Bibliogr. 13 poz.
Twórcy
autor
  • Institute of Automatic Control Silesian Technical University ul. Akademicka 16, 44-101 Gliwice, Poland
Bibliografia
  • 1. Chang K.C., Saha R.H. and Bar-Shalom Y. (1997) On optimal track to track fusion. IEEE Trans. on Aerospace and Electronic Systems 833,1271-1276.
  • 2. Chang K.C., Tian Z. and Mori S. (2004) Performance evaluation for map state estimate fusion. IEEE Trans. on Aerospace and Electronic Systems 40, 706-714.
  • 3. Chen H.M., Kirubarajan T. and Bar-Shalom Y. (2003) Performance limits on track to track fusion versus centralized estimation. IEEE Trans. on Aerospace and Electronic Systems 39, 386-400.
  • 4. Duan Z. and Li X.R. (2011) Lossless Linear Transformation of Sensor Data for Distributed Estimation Fusion. IEEE Trans. on Signal Proc. 59, 362-372.
  • 5. Hashemipour H., Roy S. and Laub A.(1988) Decentralized Structures for Parallel Kalman Filtering. IEEE Trans. Aut. Control 33, 88-93.
  • 6. Liggins M.E., Chong C.Y., Kadar I., Alford M.G., Vannicola V. and Thomopoulos S. (1997) Distributed Fusion Architectures and Algorithms for Target Tracking. Proc. of the IEEE 85, 95-107.
  • 7. Meditch J.S. (1969) Stochastic Optimal Linear Estimation and Control. Mc Graw-Hill, Inc.
  • 8. Mutambara A.G.O. (1998) Decentralized Estimation and Control for Multisensor Systems. CRC Press LLC.
  • 9. Sijs J., Lazar M., Van den Bosch P.P.J. and Papp Z. (2008) An overview of non-centralized Kalman filters. Proc. of the 17th IEEE Int. Conf. On Control Appl. IEEE, 739–744.
  • 10. Schlosser M.S. and Kroschel K. (2007) Performance analysis of decentralized Kalman Filters under Communication Constraints. Journal of Advances in Information Fusion 2, 65-75.
  • 11. Song E.B, Zhu Y.M., Zhou J. and You Z.S. (2007) Optimal Kalman filtering fusion with cross-correlated sensor noises. Automatica, 43, 1450-1456.
  • 12. Speyer J.L. (1979) Computation and Transmission Requirements for a Decentralized Linear-Quadratic-Gaussian Control Problem. IEEE Trans. Aut. Control 24, 266-269.
  • 13. Zhu Y., You Z., Zhao J., Zhang K. and Li X.R. (2001) The optima lity for the distributed Kalman filtering fusion with feedback. Automatica 37, 1489-1493.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-2789a970-11b3-4b4f-8dfd-41c0ff1cd6b5
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