Automatica (Journal of IFAC)
Technical Communique: The optimality for the distributed Kalman filtering fusion with feedback
Automatica (Journal of IFAC)
Technical Communique: Descriptor Wiener state estimators
Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Optimal linear estimation fusion .I. Unified fusion rules
IEEE Transactions on Information Theory
Decoupled distributed Kalman fuser for descriptor systems
Signal Processing
Self-tuning decoupled information fusion Wiener state component filters and their convergence
Automatica (Journal of IFAC)
Brief paper: Hybrid method for a general optimal sensor scheduling problem in discrete time
Automatica (Journal of IFAC)
Sensor Fusion in Integrated Circuit Fault Diagnosis Using a Belief Function Model
International Journal of Distributed Sensor Networks
Noise reduction method for chaotic signals based on dual-wavelet and spatial correlation
Expert Systems with Applications: An International Journal
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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
Correlated measurement fusion Kalman filters based on orthogonal transformation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Random weighting estimation for fusion of multi-dimensional position data
Information Sciences: an International Journal
Sequential covariance intersection fusion Kalman filter
Information Sciences: an International Journal
Self-tuning weighted measurement fusion Kalman filtering algorithm
Computational Statistics & Data Analysis
International Journal of Sensor Networks
Hi-index | 22.15 |
By the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, a unified and general information fusion steady-state Kalman filtering approach is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the filtering, smoothing, and prediction fusion problems for state or signal. The optimal fusion rule weighted by matrices is re-derived as a weighted least squares (WLS) fuser, and is reviewed. An optimal fusion rule weighted by diagonal matrices is presented, which is equivalent to the optimal fusion rule weighted by scalars for components, and it realizes a decoupled fusion. The new algorithms of the steady-state Kalman estimator gains are presented. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors by Lyapunov equations are presented. The exponential convergence of the iterative solution of Lyapunov equation is proved. It is proved that the optimal fusion estimators under three weighted fusion rules are locally optimal, but are globally suboptimal. The proposed steady-state Kalman fusers can reduce the on-line computational burden, and are suitable for real-time applications. A simulation example for the 3-sensor steady-state Kalman tracking fusion estimators shows their effectiveness and correctness, and gives the accuracy comparison of the fusion rules.