Convolution Particle Filter for Parameter Estimation in General State-Space Models
Convolution Particle Filter for Parameter Estimation in General State-Space Models
- New search for: Campillo, F.
- New search for: Rossi, V.
- New search for: Campillo, F.
- New search for: Rossi, V.
The state-space modeling of partially observed dynamical systems generally requires estimates of unknown parameters. The dynamic state vector together with the static parameter vector can be considered as an augmented state vector. Classical filtering methods, such as the extended Kalman filter (EKF) and the bootstrap particle filter (PF), fail to estimate the augmented state vector. For these classical filters to handle the augmented state vector, a dynamic noise term should be artificially added to the parameter components or to the deterministic component of the dynamical system. However, this approach degrades the estimation performance of the filters. We propose a variant of the PF based on convolution kernel approximation techniques. This approach is tested on a simulated case study.
Document information
Convolution Particle Filter for Parameter Estimation in General State-Space Models
IEEE Transactions on Aerospace and Electronic Systems ; 45 , 3 ; 1063-1072
2009-07-01
1409470 byte
Article (Journal)
Electronic Resource
English
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