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Gradient-free maximum likelihood parameter estimation with particle filters

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...

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Publication status:
Published

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Publisher copy:
10.1109/ACC.2006.1657187

Authors


Volume:
1-12
Pages:
3062-3067
Host title:
2006 American Control Conference, Vols 1-12
Publication date:
2006-01-01
DOI:
ISSN:
0743-1619
Source identifiers:
172721
ISBN:
1424402093
Pubs id:
pubs:172721
UUID:
uuid:ff135860-7d57-4315-8baf-7000189a3138
Local pid:
pubs:172721
Deposit date:
2012-12-19

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