Application of Girsanov theorem to particle filtering of discretely observed continuous-time non-linear systems. (English) Zbl 1330.93230

Särkkä, Simo; Sottinen, Tommi

Bayesian Anal. 3, No. 3, 555-584 (2008).

Summary: This article considers the application of particle filtering to continuous-discrete optimal filtering problems, where the system model is a stochastic differential equation, and noisy measurements of the system are obtained at discrete instances of time. It is shown how the Girsanov theorem can be used for evaluating the likelihood ratios needed in importance sampling. It is also shown how the methodology can be applied to a class of models, where the driving noise process is lower in the dimensionality than the state and thus the laws of the state and the noise are not absolutely continuous. Rao-Blackwellization of conditionally Gaussian models and unknown static parameter models is also considered.

MSC:

93E11	Filtering in stochastic control theory
62M20	Inference from stochastic processes and prediction

Keywords:

Girsanov theorem; particle filtering; continuous-discrete filtering