Dynamic Target Tracking Based on Particle Filter in Actual Environment

Article Preview

Abstract:

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

824-827

Citation:

Online since:

April 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] J. Zhu, Y. Lao, Y.F. Zheng. Object tracking in structured environments for video surveillance applications. IEEE Transactions on Circuits and Systems for Video Technology, 20 (2) (2010), p.223–235.

DOI: 10.1109/tcsvt.2009.2031395

Google Scholar

[2] K. Wei, Y.Q. Zhao, Q. Pan. IR target tracking based on mean shift and particle filter. Journal of Optoelectronics Laser, 19 (2) (2008), p.213–217 [in Chinese].

Google Scholar

[3] F. Zhou, W.J. He, X.Y. Fan. Tracking application about singer model based on marginalized particle filter. The Journal of China Universities of Posts and Telecommunications, Volume 17, Issue 4, August 2010, Pages 47-51, 124.

DOI: 10.1016/s1005-8885(09)60486-6

Google Scholar

[4] H. Han, Y.S. Ding, K.R. Hao, An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking . Computers & Mathematics with Applications, Volume 62, Issue 7, October 2011, Pages 2685-2695.

DOI: 10.1016/j.camwa.2011.06.050

Google Scholar

[5] H. Han, Y.S. Ding, K.R. Hao, An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking . Computers & Mathematics with Applications, Volume 62, Issue 7, October 2011, Pages 2685-2695.

DOI: 10.1016/j.camwa.2011.06.050

Google Scholar

[6] P. Chapelle, H. Abou-Chakra, N. Christakis. Numerical predictions of particle degradation in industrial-scale pneumatic conveyors. Powder Technology, Volumes 143–144, 25 June 2004, Pages 321-330.

DOI: 10.1016/j.powtec.2004.04.024

Google Scholar

[7] D Simon. Kalman filtering with state constraints: a survey of linear and nonlinear algorithm. IET Control Theory and Applications, 4 (8) (2010), p.1303–1318.

DOI: 10.1049/iet-cta.2009.0032

Google Scholar