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The Probabilistic Instantaneous Matching Algorithm

Beutler, Frederik; Hanebeck, Uwe D.


A new Bayesian filtering technique for estimating signal parameters directly from discrete-time sequences is introduced. The so called probabilistic instantaneous matching algorithm recursively updates the probability density function of the parameters for every received sample and, thus, provides a high update rate up to the sampling rate with high accuracy. In order to do so, one of the signal sequences is used as part of a time-variant nonlinear measurement equation. Furthermore, the time-variant nature of the parameters is explicitly considered via a system equation, which describes the evolution of the parameters over time. An important feature of the probabilistic instantaneous matching algorithm is that it provides a probability density function over the parameter space instead of a single point estimate. This probability density function can be used in further processing steps, e.g. a range based localization algorithm in the case of time-of-arrival estimation.

Volltext §
DOI: 10.5445/IR/1000013894
DOI: 10.1109/MFI.2006.265637
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2006
Sprache Englisch
Identifikator ISBN: 1-424-40566-1
KITopen-ID: 1000013894
Erschienen in Proceedings / 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 3 - 4 Sept. 2006, Heidelberg, Germany
Verlag IEEE Service Center
Seiten 311 - 316
Nachgewiesen in Dimensions
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