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Design of Data Association Filter Using Neural Networks for Multi-Target Tracking

  • Yang Weon Lee
  • Chil Woo Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

In this paper, we have developed the MHDA scheme for data association. This scheme is important in providing a computationally feasible alternative to complete enumeration of JPDA which is intractable. We have proved that given an artificial measurement and track’s configuration, MHDA scheme converges to a proper plot in a finite number of iterations. Also, a proper plot which is not the global solution can be corrected by re-initializing one or more times. In this light, even if the performance is enhanced by using the MHDA, we also note that the difficulty in tuning the parameters of the MHDA is critical aspect of this scheme. The difficulty can, however, be overcome by developing suitable automatic instruments that will iteratively verify convergence as the network parameters vary.

Keywords

Travel Salesman Problem Data Association Velocity Error Posteriori Probability Maneuvering Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yang Weon Lee
    • 1
  • Chil Woo Lee
    • 2
  1. 1.Department of Information and Communication EngineeringHonam University, SeobongdongGwangsangu, GwangjuSouth Korea
  2. 2.Department of Computer EngineeringChonnam UniversityYongbongdong, GwangjuSouth Korea

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