Development of State Estimation Filter Simulator Built on an Integrated GUI Framework

Masaru Morita; Takeshi Nishida

doi:10.20965/jaciii.2016.p0721

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JACIII Vol.20 No.5 pp. 721-729

doi: 10.20965/jaciii.2016.p0721

(2016)

Paper:

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Development of State Estimation Filter Simulator Built on an Integrated GUI Framework

Masaru Morita and Takeshi Nishida

Kyushu Institute of Technology
1-1 Sensui-cho, Tobata-ku, Kitakyushu, Fukuoka 804-8550, Japan

Received:

February 17, 2016

Accepted:

June 7, 2016

Published:

September 20, 2016

Keywords:

state estimation filter, Kalman filter, particle filter, integrated GUI simulator

Abstract

We have developed a graphical user interface (GUI)-based state estimation filter simulator (called StefAny) that makes it easy to understand and compare the behaviors of filters such as Kalman filters (KFs) and particle filters (PFs). The key feature of StefAny is to show, when a system designer applies a PF, a detailed graph representing the relationship among the distribution and weights of all particles on any arbitrary timeline through simulation. Moreover, the timeline can be specified on another graph showing an estimated time series for each filter. These features enable system designers to easily check the compatibility between a filter and a target distribution, which determines the state estimation accuracy. In this paper, we present the functions of StefAny and demonstrate in detail how StefAny facilitates understanding of the properties of filters via a compatibility check comparison experiment for PFs, point estimation methods, and distributions.

Cite this article as:

M. Morita and T. Nishida, “Development of State Estimation Filter Simulator Built on an Integrated GUI Framework,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.5, pp. 721-729, 2016.

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References

[1] A. Doucet and A. M. Johansen, “A tutorial on particle filtering and smoothing: Fifteen years later,” Handbook of Nonlinear Filtering, Vol.12, pp. 656-704, 2009.
[2] A. Doucet, A. Smith, N. d. Freitas, and N. Gordon, “Sequential Monte Carlo Methods in Practice,” Information Science and Statistics, Springer New York, 2001.
[3] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics (Intelligent Robotics and Autonomous Agents),” The MIT Press, 2005.
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[9] F. L. Lewis, L. Xie, and D. Popa, “Optimal and robust estimation: with an introduction to stochastic control theory,” Control engineering series, CRC Press, 2nd ed., 2008.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] A. Doucet and A. M. Johansen, “A tutorial on particle filtering and smoothing: Fifteen years later,” Handbook of Nonlinear Filtering, Vol.12, pp. 656-704, 2009.

[2] [2] A. Doucet, A. Smith, N. d. Freitas, and N. Gordon, “Sequential Monte Carlo Methods in Practice,” Information Science and Statistics, Springer New York, 2001.

[3] [3] S. Thrun, W. Burgard, and D. Fox, “Probabilistic Robotics (Intelligent Robotics and Autonomous Agents),” The MIT Press, 2005.

[4] [4] S. Thrun, “Particle Filters in Robotics,” Proc. of the 17th Annual Conf. on Uncertainty in AI (UAI), 2002.

[5] [5] A. Yilmaz, O. Javed, and M. Shah, “Object Tracking: A Survey,” ACM Comput. Surv., Vol.38, No.4, December 2006.

[6] [6] O. Cappé, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential Monte Carlo,” Proc. of the IEEE, Vol.95, No.5, pp. 899-924, 2007.

[7] [7] H. Driessen and Y. Boers, “MAP estimation in particle filter tracking,” Proc. of the IETthe IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, pp. 41-45, IET, 2008.

[8] [8] S. Saha, Y. Boers, H. Driessen, P. K. Mandal, and A. Bagchi, “Particle based MAP state estimation: A comparison,” 12th Int. Conf. on Information Fusion 2009 (FUSION’09), pp. 278-283, 2009.

[9] [9] F. L. Lewis, L. Xie, and D. Popa, “Optimal and robust estimation: with an introduction to stochastic control theory,” Control engineering series, CRC Press, 2nd ed., 2008.