Globally Optimal Multisensor Distributed Random Parameter Matrices Kalman Filtering Fusion with Applications.
Sensors (Basel)
; 8(12): 8086-8103, 2008 Dec 08.
Article
en En
| MEDLINE
| ID: mdl-27873977
ABSTRACT
This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Clinical_trials
Idioma:
En
Revista:
Sensors (Basel)
Año:
2008
Tipo del documento:
Article