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Volume 31 Issue 3
Dec.  2010
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JIA Yunjian, HUANG Yu, LIANG Liang, WAN Yangliang, ZHOU Jihua. Research on Hierarchical Federated Learning Incentive Mechanism Based on Master-Slave Game[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1366-1373. doi: 10.11999/JEIT220175
Citation: Deng Zi-li, Gu Lei, Ran Chen-jian. Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity[J]. Journal of Electronics & Information Technology, 2009, 31(3): 556-560. doi: 10.3724/SP.J.1146.2007.01530

Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity

doi: 10.3724/SP.J.1146.2007.01530
  • Received Date: 2007-12-19
  • Rev Recd Date: 2008-04-14
  • Publish Date: 2009-03-19
  • For the multisensor linear discrete time-invariant stochastic control systems with correlated input and measurement white noises, and with correlated measurement white muses, a weighted measurement fusion steady-state Kalman filtering algorithm is presented by using the Weighted Least Squares(WLS)method. It can handle the fused filtering , smoothing and prediction problems for the state, white noise and signal. Based on the steady-state information filter, it is proved that it is completely functionally equivalent to the centralized measurement fusion steady-state Kalman filtering algorithm, so that it has asymptotic global optimality, and can reduced the computational burden. A simulation examples for tracking systems verifies its functional equivalence.
  • Gan Q and Harris C J. Comparison of two measurementfusion methods for Kalman filter-based mutisensor datafusion[J].IEEE Trans. on Aerospace and Electronic Systems.2001, 37(1):273-279[2]Roecker J A and McGillen C D. Comparison of two-sensortracking methods based on state vector fusion andmeasurement fusion. IEEE Trans. on Aerospace andElectronic Systems, 1988, 21(4): 447-449.[3]Li X R, Zhu Y M, and Wang J, et al.. Optimal linearestimation fusion-part I: Unified fusion rules[J].IEEE Trans. onInformation Theory.2003, 49(9):2192-2208[4]Hall D L and Llinas J. An introduction to multisensor datafusion[J].Proc. IEEE.1997, 85(1):6-23[5]Deng Z L, Gao Y, and Mao L, et al.. New approach toinformation fusion steady-state Kalman filtering[J].Automatica.2005, 41(10):1695-1707[6]邓自立. 两种最优观测融合方法的功能等价性. 控制理论与应用, 2006, 23(2): 319-323.Deng Zi-li. On functional equivalence of two measurementfusion methods. Control Theory Applications, 2006, 23(2):319-323.[7]Roy S and Iltis R A. Decentralized linear estimation incorrelated measurement noise[J].IEEE Trans. on Aerospaceand Electronic System.1991, 27(6):939-941[8]欧连军, 邱红专, 张洪钺. 多个相关测量的融合算法及其最优性. 信息与控制, 2005, 34(6): 690-695.Ou Lian-jun, Qiu Hong-zhuan, and Zhang Hong-yue.Multiple correlated measurement fusion algorithm and itsoptimality. Information and Control, 2005, 34(6): 690-695.[9]惠玉松, 顾磊, 冉陈键, 邓自立. 基于稳态Kalman滤波的相关观测融合算法及其功能等价性.科学技术与工程, 2007,7(19): 4809-4814.Hui Yu-song, Gu Lei, Ran Chen-jian, and Deng Zi-li.Correlated measurements fusion methods based steady-stateKalman filtering and their functional equivalence. ScienceTechnology and Engineering, 2007, 7(19): 4809-4814.[10]Darouach M, Zasdzinshi M, and Onana A B, et al.. Kalmanfilteing with unknown inputs via optimal state estimation.Int. J. Systerms, 1995, 20(10): 2015-2028.[11]Kailath T A, Sayed H, and Hassibi B. Linear Estimation,Upper Sddle River, New Jersey : Prentice-Hall, 2000: 41-333.[12]邓自立. 最优估计理论及其应用建模、滤波、信息融合估计. 哈尔滨: 哈尔滨工业大学出版社, 2005: 150-154.Deng Zi-li. Optional Estimation Theory with Applications,Modeling, Filtering, and Information Fusion. Harbin: HarbinInstitute of Technology Press, 2005: 150-154.[13]Chui C K and chen G. Kalman Filterig with Real TimeApplications. New York: Spring-Verlag Berlin Heidelberg,1987: 91-95.[14]邓自立. 信息融合滤波理论及其应用. 哈尔滨: 哈尔滨工业大学出版社, 2007: 119-123.Deng Zi-li. Information Fusion Filterig Theory withApplications. Harbin: Harbin Institute of Technology Press,2007: 119-123.
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