NDLI: Optimal dirac approximation by exploiting independencies

Content Provider	IEEE Xplore Digital Library
Author	Eberhardt, H. Klumpp, V. Hanebeck, U.D.
Copyright Year	2010
Description	Author affiliation: Intell. Sensor-Actuator-Syst. Lab. (ISAS), Karlsruhe Inst. of Technol., Karlsruhe, Germany (Eberhardt, H.; Klumpp, V.; Hanebeck, U.D.)
Abstract	The sample-based recursive prediction of discrete-time nonlinear stochastic dynamic systems requires a regular reapproximation of the Dirac mixture densities characterizing the state estimate with an exponentially increasing number of components. For that purpose, a systematic approximation method is proposed that is deterministic and guaranteed to minimize a new type distance measure, the so called modified Cramér-von Mises distance. A huge increase in approximation performance is achieved by exploiting structural independencies usually occurring between the random variables used as input to the system. The corresponding prediction step achieves optimal performance when no further assumptions can be made about the system function. In addition, the proposed approach shows a much better convergence compared to the prediction step of the particle filter and by far fewer Dirac components are required for achieving a given approximation quality. As a result, the new approximation method opens the way for the development of new fully deterministic and optimal stochastic state estimators for nonlinear dynamic systems.
Starting Page	1392
Ending Page	1398
File Size	2160526
Page Count	7
File Format	PDF
ISBN	9781424474264
ISSN	07431619
e-ISBN	9781424474271
DOI	10.1109/ACC.2010.5530503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	State estimation Particle filters Stochastic systems Approximation methods Random variables Convergence Density measurement Bayesian methods Optimal control Nonlinear control systems
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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