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Research Papers

Modified Maximum Entropy Fuzzy Data Association Filter

[+] Author and Article Information
Abdolreza Dehghani Tafti

Department of Electrical Engineering, Islamic Azad University, Science and Research Branch, Tehran 1477893855, Irandehghani@kiau.ac.ir

Nasser Sadati

Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Department of Electrical Engineering, Sharif University of Technology, Tehran 1458889694, Iransadati@ece.ubc.ca

J. Dyn. Sys., Meas., Control 132(2), 021013 (Feb 09, 2010) (9 pages) doi:10.1115/1.4000817 History: Received December 30, 2008; Revised December 01, 2009; Published February 09, 2010; Online February 09, 2010

The problem of fuzzy data association for target tracking in a cluttered environment is discussed in this paper. In data association filters based on fuzzy clustering, the association probabilities of tracking filters are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. Clearly in these filters, the fuzzy clustering method has an important role; better approach causes better precision in target tracking. Recently, by using the information theory, the maximum entropy fuzzy data association filter (MEF-DAF), as a fast and efficient algorithm, is introduced in literature. In this paper, by modification of a fuzzy clustering objective function, which is prepared for using in target tracking, a modified maximum entropy fuzzy data association filter (MMEF-DAF) is proposed. The MMEF-DAF has a better performance in case of single and multiple target tracking than MEF-DAF, and the other known algorithms such as probabilistic data association filter and the hybrid fuzzy data association filter. Using Monte Carlo simulations, the superiority of the proposed algorithm in comparison with the previous ones is demonstrated. Simply, less computational cost and suitability for real-time applications are the main advantages of the proposed algorithm.

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Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 2

The rms tracking error in the single target tracking

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Figure 5

The rms tracking error in tracking of two crossing targets

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Figure 6

The rms tracking error of 50 Monte Carlo simulations in tracking of two crossing targets

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Figure 7

The actual positions and measurements of three parallel targets

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Figure 8

The rms tracking error in tracking of three parallel targets

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Figure 9

The rms tracking error of 50 Monte Carlo simulations in tracking of three parallel targets

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Figure 1

The actual position and measurements of a single target

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Figure 3

The rms tracking error of 50 Monte Carlo simulations in the single target tracking

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Figure 4

The actual positions and measurements of two crossing targets

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