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Research Papers

Adaptive Neuro-Fuzzy Inference System in Fuzzy Measurement to Track Association

[+] Author and Article Information
Abdolreza Dehghani Tafti

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

Nasser Sadati

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

J. Dyn. Sys., Meas., Control 132(2), 021009 (Feb 04, 2010) (8 pages) doi:10.1115/1.4000663 History: Received December 30, 2008; Revised October 07, 2009; Published February 04, 2010; Online February 04, 2010

The main issue in a surveillance environment is the target tracking. The most important concern in this problem is the association of the various measurements with the existing target tracks. The fuzzy c-means data association (FCMDA) algorithm, based on the fuzzy c-means (FCM) algorithm, is an efficient solution for the problem of measurement to track association in a multisensor multitarget environment. It has a high accuracy in measurement to track association when targets are far from each other. However, its accuracy remains low when targets are close to one another. The FCMDA algorithm performance is usually lost in this environment, especially when measurement noise is high. In the FCMDA algorithm, the association between measurements and tracks is determined using an optimal membership function derived from the FCM algorithm for the fixed predicted state of targets. The prediction of the target state deviates from its correct value based on updating the tracker/filter with the wrong associated measurement. Consequently, the wrong association can take place using a deviated prediction of target state in the FCMDA algorithm. In this paper, to overcome this shortcoming of the FCMDA algorithm, the predicted state of every target in a surveillance environment is compensated for the effect of wrong associated measurement by an adaptive neurofuzzy inference system (ANFIS). An ANFIS has both the advantages of expert knowledge of a fuzzy inference system and the learning capability of neural networks. So a trained ANFIS is able to compensate the effect of a wrong associated measurement on the prediction of target state. Using the compensated prediction of target state in the FCMDA algorithm can always save the performance of the FCMDA algorithm and extend its domain of usage in practical applications. The simulation results demonstrate that considerable improvements in terms of accuracy and performance are achieved by using the compensated prediction of target state in the FCMDA algorithm.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

The general architecture of an ANFIS

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Figure 2

The FCMDA algorithm with an ANFIS

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Figure 3

Actual and estimated tracks using the FCMDA algorithm in scenario 1

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Figure 4

Actual and estimated tracks using the FCMDA algorithm with an ANFIS in scenario 1

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Figure 5

rms tracking errors using FCMDA, FCMDA with an ANFIS, and perfect data association in scenario 1

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Figure 6

Actual and estimated tracks using the FCMDA algorithm in scenario 2

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Figure 7

Actual and estimated tracks using the FCMDA algorithm with an ANFIS in scenario 2

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Figure 8

rms tracking errors using FCMDA, FCMDA with an ANFIS, and perfect data association in scenario 2

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Figure 9

Actual and estimated tracks using the FCMDA algorithm in scenario 3

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Figure 10

Actual and estimated tracks using the FCMDA algorithm with an ANFIS in scenario 3

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Figure 11

rms tracking errors using FCMDA, FCMDA with an ANFIS, and perfect data association in scenario 3

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