- Abstract:
-
In this paper we address the problem of on-line estimation of unknown static parameters in non-linear non-Gaussian state-space models. We consider a particle filtering method and employ two gradient-free Stochastic Approximation (SA) methods to maximize recursively the likelihood function, the Finite Difference SA and Spall's Simultaneous Perturbation SA. We demonstrate how these algorithms can generate maximum likelihood estimates in a simple and computationally efficient manner. The perform...
Expand abstract - Publication status:
- Published
- Publisher copy:
- 10.1109/ACC.2006.1657187
-
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- Volume:
- 1-12
- Pages:
- 3062-3067
- Publication date:
- 2006-01-01
- DOI:
- ISSN:
-
0743-1619
- URN:
-
uuid:ff135860-7d57-4315-8baf-7000189a3138
- Source identifiers:
-
172721
- Local pid:
- pubs:172721
- ISBN:
- 1-4244-0209-3
- Copyright date:
- 2006
Conference
Gradient-free maximum likelihood parameter estimation with particle filters
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