DocumentCode :
262793
Title :
Deterministic Dirac mixture approximation of Gaussian mixtures
Author :
Gilitschenski, Igor ; Steinbring, Jannik ; Hanebeck, Uwe D. ; Simandl, Miroslav
Author_Institution :
Intell. Sensor-Actuator-Syst. Lab. (ISAS), Karlsruhe Inst. of Technol. (KIT), Karlsruhe, Germany
fYear :
2014
fDate :
7-10 July 2014
Firstpage :
1
Lastpage :
7
Abstract :
In this work, we propose a novel way to approximating mixtures of Gaussian distributions by a set of deter-ministically chosen Dirac delta components. This approximation is performed by adapting a method for approximating single Gaussian distributions to the considered case. The proposed method turns the approximation problem into an optimization problem by minimizing a distance measure between the Gaussian mixture and its Dirac mixture approximation. Compared to the simple Gaussian case, the minimization criterion is much more complex as multiple, non-standard Gaussian distributions have to be considered.
Keywords :
Gaussian distribution; approximation theory; minimisation; Dirac delta components; Gaussian distribution; Gaussian mixture; deterministic Dirac mixture approximation; distance measure; minimization criterion; optimization problem; single Gaussian distribution; Approximation methods; Gaussian distribution; Kalman filters; Kernel; Optimization; Probability distribution; Shape; Deterministic sampling; nonlinear propagation; shape approximation; statistical distance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2014 17th International Conference on
Conference_Location :
Salamanca
Type :
conf
Filename :
6916002
Link To Document :
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