Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter

For the multisensor systems with unknown noise variances, based on the solution of the matrix equations for the correlation function, the on-line estimators of the noise variance matrices are obtained, whose consistency is proved using the ergodicity of sampled correlation function. Further, two sel...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2009-01, Vol.45 (1), p.179-191
Main Authors: Gao, Yuan, Jia, Wen-Jing, Sun, Xiao-Jun, Deng, Zi-Li
Format: Article
Language:English
Subjects:
Convergence
Correlation
Dynamic tests
Dynamical systems
Dynamics
Equations
Error analysis
Kalman filters
Mathematical analysis
Matrices
Multisensor systems
Sensor systems
Stability analysis
Steady-state
Studies
Target tracking
Weight measurement
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Summary:For the multisensor systems with unknown noise variances, based on the solution of the matrix equations for the correlation function, the on-line estimators of the noise variance matrices are obtained, whose consistency is proved using the ergodicity of sampled correlation function. Further, two self-tuning weighted measurement fusion Kalman filters are presented for the multisensor systems with identical and different measurement matrices, respectively. Based on the stability of the dynamic error system, a new convergence analysis tool is presented for a self-tuning fuser, which is called the dynamic error system analysis (DESA) method. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. It is rigorously proved that the proposed self-tuning Kalman fusers converge to the steady-state optimal Kalman fusers in a realization or with probability one, so that they have asymptotic global optimality. A simulation example for a target tracking system with 3 sensors shows their effectiveness.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2009.4805272