On the Improved Particle Filter by Mahalanobis Distance Consideration 


Vol. 46,  No. 6, pp. 1023-1029, Jun.  2021
10.7840/kics.2021.46.6.1023


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  Abstract

In this paper, Enhanced Particle Filter by Mahalanobis distance consideration is proposed. Particle filter, one of Kalman Filter, is state estimation algorithm that works well in practical environments with non-linear data and non-gaussian noise. Mahalanobis distance measures the distance on a probability distribution, unlike Euclidean distance, which measures distance in dimensional space. Simulation is performed on a non-linear falling object model. Comparison of performance of the proposed versus the original is indicated by RMS error in the simulation result.

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  Cite this article

[IEEE Style]

J. Park, W. Lim, Y. Yang, "On the Improved Particle Filter by Mahalanobis Distance Consideration," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 6, pp. 1023-1029, 2021. DOI: 10.7840/kics.2021.46.6.1023.

[ACM Style]

Jeong-hun Park, Wansu Lim, and Yeon-Mo Yang. 2021. On the Improved Particle Filter by Mahalanobis Distance Consideration. The Journal of Korean Institute of Communications and Information Sciences, 46, 6, (2021), 1023-1029. DOI: 10.7840/kics.2021.46.6.1023.

[KICS Style]

Jeong-hun Park, Wansu Lim, Yeon-Mo Yang, "On the Improved Particle Filter by Mahalanobis Distance Consideration," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 6, pp. 1023-1029, 6. 2021. (https://doi.org/10.7840/kics.2021.46.6.1023)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880