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Steady-state Kalman Fusion Filter Based on Improved Multi-innovation Least Squares Algorithm

2018 37th Chinese Control Conference (CCC), July 2018, pp.4434-4437

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  • Title:
    Steady-state Kalman Fusion Filter Based on Improved Multi-innovation Least Squares Algorithm
  • Author: Ke, Zhao ; Ying, Shi
  • Found In: 2018 37th Chinese Control Conference (CCC), July 2018, pp.4434-4437
  • Subjects: Improved Multi-Innovation Least Squares ; Steady-State Kalman Filter ; Matrix Weighted Fusion ; Engineering
  • Language: English
  • Description: Based on the improved multi-innovation least squares algorithm and Kalman filtering method, the state fusion estimation of multi-sensor systems with unknown parameters is studied. Firstly, an improved multi-innovation least squares algorithm is proposed to identify the unknown model parameters of the system. Then, based on the results of model parameter identification, the steady-state fusion Kalman filter of multi-sensor system is given by using the matrix weighted fusion criterion. A numerical simulation example verifies the effectiveness of the proposed algorithm.
  • Identifier: E-ISSN: 2161-2927 ; E-ISBN: 9789881563958 ; E-ISBN: 988156395X ; DOI: 10.23919/ChiCC.2018.8483970

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