Recent Results on Bayesian Cramer-Rao Bounds for Jump Markov Systems
Paper in proceeding, 2016
In this paper, recent results on the evaluation of the Bayesian Cramer-Rao bound for jump Markov systems are presented. In particular, previous work is extended to jump Markov systems where the discrete mode variable enters into both the process and measurement equation, as well as where it enters exclusively into the measurement equation. Recursive approximations are derived with finite memory requirements as well as algorithms for checking the validity of these approximations are established. The tightness of the bound and the validity of its approximation is investigated on a couple of examples.
Computational modeling
Bayes methods
Mathematical model
Markov processes
State
Cramer-Rao bounds
Engineering
Filter
Computer Science
Approximation algorithms
Monte Carlo methods
Author
C. Fritsche
U. Orguner
Lennart Svensson
Chalmers, Signals and Systems
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
F. Gustafsson
2016 19th International Conference on Information Fusion (Fusion)
512-520
978-0-9964-5274-8 (ISBN)
Driving Forces
Sustainable development
Subject Categories
Electrical Engineering, Electronic Engineering, Information Engineering
Signal Processing
Areas of Advance
Materials Science
ISBN
978-0-9964-5274-8