A Decentralized Multiple Model Adaptive Filtering for Discrete-Time Stochastic Systems

1989 ◽  
Vol 111 (3) ◽  
pp. 371-377 ◽  
Author(s):  
Keigo Watanabe
Keyword(s):  
Discrete Time ◽  
Kalman Filtering ◽  
Adaptive Filtering ◽  
Stochastic Systems ◽  
Failure Detection ◽  
Multiple Model ◽  
Sensor Failure ◽  
Lateral Dynamics ◽  
Detection And Identification ◽  
Partial Element

A decentralized multiple model adaptive filter (MMAF) is proposed for linear discrete-time stochastic systems. The structure of decentralized multiple model studied here is based on introducing a global hypothesis for the global model and a local hypothesis for the local model, where it is assumed that the former hypothesis includes the latter one as a partial element. Algorithms for the decentralized MMAFs in unsteady and steady-state are derived using recent results in decentralized Kalman filtering. The results can be applied in designing a system for sensor failure detection and identification (FDI). An example is included to illustrate the characteristics of such a FDI system for the estimation of lateral dynamics of the hydrofoil boat.

Fixed-Interval Kalman Smoother for Non-Square Descriptor Systems with Correlated Noise

2012 ◽  
Vol 433-440 ◽  
pp. 3601-3607
Author(s):  
Hang Wei Tian ◽  
Ying Shi
Keyword(s):  
State Estimation ◽  
Discrete Time ◽  
Kalman Filtering ◽  
Stochastic Systems ◽  
Fixed Interval ◽  
Descriptor Systems ◽  
Correlated Noise ◽  
Filtering Theory ◽  
Numerical Examples ◽  
Kalman Smoother

Based on the classical Kalman filtering theory, the state estimation problem is considered for non-square descriptor discrete time stochastic systems. Under Assumptions 1~3, a fixed-Interval Kalman smoother for non-square descriptor systems with correlated noise is given. Some numerical examples illustrate the effectiveness of the proposed algorithm.

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