Konferenční příspěvek

Ajgl J., Šimandl M.

Proceeding of the 14th International Conference on Information Fusion

ISIF

Chicago, Illinois, USA

2011

803-810

978-0-9824438-2-8

Ing. Jiří Ajgl (FAV Západočeská univerzita, katedra kybernetiky)

Data fusion, nonlinear estimation, particle filters

clanek_fusion11_AS.pdf [file]

prezentace_fusion11_AS.pdf [file]

This paper focuses on a decentralised nonlinear estimation problem in a multiple sensor network. The stress is laid on the optimal fusion of probability densities conditioned by different data. The probability density conditioned by the common data is supposed to be unavailable. The optimal fusion is elaborated in the particle ?ltering and differential Shannon entropy framework. The conversion of weighted particles into a continuous probability density function is performed implicitly by the time update. Further, the issue of sampling density proposal is explored. The proposed approach is illustrated in numerical examples.