Advertisement

A Real-Time Moving Ant Estimator for Bearings-Only Tracking

  • Jihong Zhu
  • Benlian Xu
  • Fei Wang
  • Zhiquan Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

A real-time moving ant estimator (RMAE) is developed for the bearings-only target tracking, in which ants located at their individual current state utilize the normalized weight and pheromone value to select the one-step prediction state and the dynamic moving velocity of each ant is depended directly on the normalized weights between two states. Besides this, two pheromone update strategy is implemented. Numerical simulations indicate that the RMAE could estimate adaptively the state of maneuvering or non-maneuvering target, and real-time requirement is superior to the moving ant estimator (MAE).

Keywords

Bearings-only Ant colony optimization Parameter estimation Target tracking 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: Proceedings of Aerosense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, vol. 3068, pp. 182–193 (1997)Google Scholar
  2. 2.
    Särkkä, S., Vehtari, A., Lampinem, J.: Rao-Blackwellized particle filter for multiple target tracking. Information Fusion 8, 2–15 (2007)CrossRefGoogle Scholar
  3. 3.
    Yu, Y.H., Cheng, Q.S.: Particle filter for maneuvering target tracking problem. Signal Processing 86, 195–203 (2006)zbMATHCrossRefGoogle Scholar
  4. 4.
    Carpenter, J., Clifford, P., Fearnhead, P.: Improved particle filter for nonlinear problems. IEE Proceedings of Radar, Sonar and Navigation 146(1), 2–7 (1999)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Transactions on System, Man and Cybernetics-Part B 26(1), 29–42 (1996)CrossRefGoogle Scholar
  6. 6.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: From nature to artificial intelligence. Oxford University Press, New York (1999)Google Scholar
  7. 7.
    Nolle, L.: On a novel ACO-estimator and its application to the target motion analysis problem. Knowledge-Based Systems 21(3), 225–231 (2008)CrossRefGoogle Scholar
  8. 8.
    Xu, B.L., Chen, Q.L., Zhu, J.H., Wang, Z.Q.: Ant estimator and its application to target tracking. Signal Processing (2009) (in press)Google Scholar
  9. 9.
    Xu, B.L., Chen, Q.L., Wang, X.Y., Zhu, J.H.: A novel estimator with moving ants. Simulation Modelling Practice and Theory 17, 1663–1677 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jihong Zhu
    • 1
  • Benlian Xu
    • 2
  • Fei Wang
    • 1
  • Zhiquan Wang
    • 1
  1. 1.School of AutomationNanjing University of Science and TechnologyNanjingP.R. China
  2. 2.Electrical and Automatic EngineeringChangshu Institute of TechnologyChangshuP.R. China

Personalised recommendations