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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BSW3-0103-0016

Czasopismo

Archives of Control Sciences

Tytuł artykułu

Hierarchical filtration for distributed linear multisensor systems

Autorzy Duda, Z. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN In the paper two filtration algorithms for distributed multisensor system are presented. The first one is derived for a linear dynamical system composed of local subsystems described by local state equations. Local estimates are sent to a central station to be fused and formed an optimal global estimate. The second algorithm is derived for a system observed by local nodes that determine estimates of the whole system using local information and periodically aggregated information from other nodes. Periodically local estimates are sent to the central station to be fused. Owing to this a reduced communication can be achieved.
Słowa kluczowe
EN multisensor system   distributed Kalman filtering   decentralized filtration   hierarchical fusion   aggregated information  
Wydawca Polish Academy of Sciences, Committee of Automation and Robotics
Czasopismo Archives of Control Sciences
Rocznik 2012
Tom Vol. 22, no. 4
Strony 507--518
Opis fizyczny Bibliogr. 14 poz., rys., tab.
Twórcy
autor Duda, Z.
  • Institute of Control, Silesian Technical University, Poland
Bibliografia
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[2] 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.
[3] H. M. Chen, T. Kirubarajan and Y. Bar-Shalom: Performance limits on track to track fusion versus centralized estimation. IEEE Trans. on Aerospace andElectronic Systems, 39 (2003), 386-400.
[4] 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.
[5] H. Hashmipour, S. Roy and A. Laub: Decentralized Structures for Parallel Kalman Filtering. IEEE Trans. Aut. Control, 33 (1988), 88-93.
[6] M. E. Liggins at all: Distributed Fusion Architectures and Algorithms for Target Tracking. Proc. of the IEEE, 85 (1997), 895-107.
[7] 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 Appl., USA, (2008).
[8] M. S. Schlosser and K. Kroschel: Performance analysis of decentralized Kalman Filters under Communication Constraints. J. of Advances in InformationFusion, 2 (2007), 65-75.
[9] 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.
[10] K. S. Zhang, X. R. Li and H. F. Li: Optimal linear estimation fusion-Part VI: Sensor data compression. Proc. of the 6th Int. Conf. Information Fusion, Australia, (2003).
[11] Y. M. Zhu, E. B. Song, J. Shou and Z.S. You: Optimal dimensionality reduction of sensor data in multisensor estimation fusion. IEEE Trans. on Signal Proc., 53 (2005), 1631-1639.
[12] E. B. Song, Y. M. Zhu and J. Zhou: Sensors optimal dimensionality compression matrix in estimation fusion. Automatica, 41 (2005), 2131-2139.
[13] I. D. Schizas, G. B. Giannakis and Z.Q. Luo: Distributed estimation using reduced-dimensionality sensor observations. IEEE Trans. on Signal Proc., 535 (2007), 4284-4299.
[14] S. Julier and J. Uhlmann: A non-divergent estimation algorithm in the presence of unknown correlations. Proc. of American Control Confer., USA, (1997).
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