Bayesian Cramer-Rao Bound for Nonlinear Filtering with Dependent Noise Processes
2013 (English)In: 16th International Conference on Information Fusion (FUSION 2013), IEEE , 2013, 797-804 p.Conference paper (Refereed)
The Bayesian Cramer Rao Bound (BCRB) is derived for nonlinear state space models with dependent process and measurement noise processes. It generalizes the previously BCRB for the case of dependent noise. Two different dependence structures appearing in literature are considered, leading to two different recursions for BCRB. The special cases of Gaussian noise, and linear models are presented separately. Simulations demonstrate that correct treatment of dependencies is important for both filtering algorithms and the BCRB.
Place, publisher, year, edition, pages
IEEE , 2013. 797-804 p.
IdentifiersURN: urn:nbn:se:liu:diva-103982ISI: 000341370000106ISBN: 978-605-86311-1-3OAI: oai:DiVA.org:liu-103982DiVA: diva2:693539
16th International Conference on Information Fusion(FUSION 2013), Istanbul, Turkey, July 9-12, 2013
FundereLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS