System identification: theory for the user
System identification: theory for the user
Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
Automatica (Journal of IFAC)
Technical Communique: Descriptor Wiener state estimators
Automatica (Journal of IFAC)
New approach to information fusion steady-state Kalman filtering
Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
A self-tuning filter for fixed-lag smoothing
IEEE Transactions on Information Theory
Information fusion estimation of noise statistics for multisensor systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Self-tuning weighted measurement fusion Wiener filter and its convergence
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Self-tuning measurement fusion white noise deconvolution estimator
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
The convergence analysis of the self-tuning Riccati equation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Sequential covariance intersection fusion Kalman filter
Information Sciences: an International Journal
Self-tuning weighted measurement fusion Kalman filtering algorithm
Computational Statistics & Data Analysis
Digital Signal Processing
Hi-index | 22.14 |
For the multisensor systems with unknown noise variances, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances are obtained, and under linear minimum variance optimal information fusion criterion weighted by scalars for state components, a class of self-tuning decoupled fusion Wiener filters is presented. It realizes the self-tuning decoupled local Wiener filters and self-tuning decoupled fused Wiener filters for the state components. A new concept of convergence in a realization is presented, which is weaker than the convergence with probability one. The dynamic error system analysis (DESA) method is presented, by which the problem of convergence in a realization for self-tuning fusers is transformed into the stability problems of non-homogeneous difference equations, and the decision criterions of the stability are also presented. It is strictly proved that if the parameter estimation of the MA innovation models is consistent and if the measurement process is bounded in a realization or with probability one, then the self-tuning fusers will converge to the optimal fusers in a realization or with probability one, so that they have the asymptotic optimality. They can deal with the systems with the non-stationary or Gaussian measurement processes. They can reduce the computational burden, and are suitable for real time applications. A simulation example for a target tracking system with 3-sensor shows their effectiveness.