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Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter

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4 Author(s)
Yuan Gao ; Dept. of Autom., Heilongjiang Univ., Harbin ; Wen-Jing Jia ; Xiao-Jun Sun ; Zi-li Deng

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.

Published in:

Aerospace and Electronic Systems, IEEE Transactions on  (Volume:45 ,  Issue: 1 )

Date of Publication:

Jan. 2009

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