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Data Association in Multi-target Tracking Using Belief Function

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Abstract

In this paper, we present a new method for data association in multi-target tracking. The representation and the fusion of the information in our method are based on the use of belief function. The proposal generates the basic belief mass assignment using a modified Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses. Our method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases. The results demonstrate the feasibility of the proposal and show improved performance compared to the aforementioned alternative commonly used methods.

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Authors and Affiliations

  1. Electronic Systems Laboratory, Military Polytechnic School, BP 17, Bordj El Bahri Algiers, Algeria

    Ahmed Dallil & Abdelaziz Ouldali

  2. University of Birmingham, Electronics, Electrical and Computer Engineering Edgbaston, Birmingham, B15 2TT, UK

    Ahmed Dallil & Mourad Oussalah

Authors
  1. Ahmed Dallil
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  2. Abdelaziz Ouldali
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  3. Mourad Oussalah
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Correspondence to Ahmed Dallil.

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Dallil, A., Ouldali, A. & Oussalah, M. Data Association in Multi-target Tracking Using Belief Function. J Intell Robot Syst 67, 219–227 (2012). https://doi.org/10.1007/s10846-011-9640-y

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  • DOI: https://doi.org/10.1007/s10846-011-9640-y

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