Distance-based Pruning for Gaussian Sum Method in Non-Gaussian System State Estimation

O. Straka and M. Šimandl (Czech Republic)


Stochastic systems, nonlinear estimation, Gaussian sum, pruning and merging, probability density function


State estimation of the non-Gaussian systems by the Gaus sian sum method is treated. The distance-based pruning technique is designed for an approximation of the filter ing probability density function given by a weighted sum of Gaussian distributions. The technique measures signif icance of each term of the sum using the Lissack-Fu dis tance between the approximate filtering probability den sity function and the filtering probability density function and prunes the insignificant terms. The paper also pro poses a thrifty implementation of the developed technique. The distance-based pruning technique provides high ap proximation quality in comparison with other approxima tion techniques, moreover it achieves low computational demands as it is illustrated in a numerical example.

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