NDLI: On the Bayesian Cramér-Rao Bound for Markovian Switching Systems

Content Provider	IEEE Xplore Digital Library
Author	Svensson, L.
Copyright Year	1991
Abstract	We propose a numerical algorithm to evaluate the Bayesian Cramér-Rao bound (BCRB) for multiple model filtering problems. It is assumed that the individual models have additive Gaussian noise and that the measurement model is linear. The algorithm is also given in a recursive form, making it applicable for sequences of arbitrary length. Previous attempts to calculate the BCRB for multiple model filtering problems are based on rough approximations which usually make them simple to calculate. In this paper, we propose an algorithm which is based on Monte Carlo sampling, and which is hence more computationally demanding, but yields accurate approximations of the BCRB. An important observation from the simulations is that the BCRB is more overoptimistic than previously suggested bounds, which we motivate using theoretical results.
Sponsorship	IEEE Signal Processing Society
Starting Page	4507
Ending Page	4516
Page Count	10
File Size	351455
File Format	PDF
ISSN	1053587X
Volume Number	58
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Switching systems Filtering algorithms Switches Additive noise Gaussian noise Noise measurement Monte Carlo methods Computational modeling performance bounds Cramér–Rao bound jump Markov system maneuvering target multiple model filtering non-linear filtering
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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