NDLI: Hybrid Transition Density Approximation for Efficient Recursive Prediction of Nonlinear Dynamic Systems

Content Provider	IEEE Xplore Digital Library
Author	Huber, M.F. Hanebeck, U.D.
Copyright Year	2007
Description	Author affiliation: Univ. Karlsruhe, Karlsruhe (Huber, M.F.; Hanebeck, U.D.)
Abstract	For several tasks in sensor networks, such as localization, information fusion, or sensor scheduling, Bayesian estimation is of paramount importance. Due to the limited computational and memory resources of the nodes in a sensor network, evaluation of the prediction step of the Bayesian estimator has to be performed very efficiently. An exact and closed-form representation of the predicted probability density function of the system state is typically impossible to obtain, since exactly solving the prediction step for nonlinear discrete-time dynamic systems in closed form is unfeasible. Assuming additive noise, we propose an accurate approximation of the predicted density, that can be calculated efficiently by optimally approximating the transition density using a hybrid density. A hybrid density consists of two different density types: Dirac delta functions that cover the domain of the current density of the system state, and another density type, e.g. Gaussian densities, that cover the domain of the predicted density. The freely selectable, second density type of the hybrid density depends strongly on the noise affecting the nonlinear system. So, the proposed approximation framework for nonlinear prediction is not restricted to a specific noise density. It further allows an analytical evaluation of the Chapman-Kolmogorov prediction equation and can be interpreted as a deterministic sampling estimation approach. In contrast to methods using random sampling like particle filters, a dramatic reduction in the number of components and a subsequent decrease in computation time for approximating the predicted density is gained.
Sponsorship	ACM SIGBED IEEE Signal Process. Soc.
Starting Page	283
Ending Page	292
File Size	6369156
Page Count	10
File Format	PDF
ISBN	9781595936387
DOI	10.1109/IPSN.2007.4379688
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Performance evaluation Additive noise Probability Density Approximation Theory Sensor fusion Hybrid Density Nonlinear dynamical systems Processor scheduling Algorithms Bayesian methods Probability density function Nonlinear Prediction Sampling methods Computer networks Current density Recursive Bayesian Estimation
Content Type	Text
Resource Type	Article

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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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