Skip to main content

Dim Target Tracking Base on GM-PHD Filter

  • Conference paper
Intelligent Science and Intelligent Data Engineering (IScIDE 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7202))

  • 3524 Accesses

Abstract

In this paper, a real time method for detecting and tracking multiple dim targets in deep space background is presented. We matched the stars in tow continuous images to get their speed at first and found moving targets through speed in both images. Using the targets in the common frame data association is achieved. The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is used to track targets to solve the problem of targets disappearance. To initialize of the birth random finite sets (RFSs) the targets sequences are built to find new targets. Extensive experiments on real images sequences show that the proposed approach could effectively meet the requirements of the real-time detection with a low false alarm rate and a high detection probability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mengdao, X., Junhai, S., Genyuan, W., Zheng, B.: New Parameter Estimation and Detection Algorithm for High Speed Small Target. IEEE Transactions on Aerospace and Electronic Systems 47, 214–224 (2011)

    Article  Google Scholar 

  2. Cossio, T.K., Slatton, K.C., Carter, W.E., Shrestha, K.Y., Harding, D.: Predicting Small Target Detection Performance of Low-SNR Airborne Lidar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3, 672–688 (2010)

    Article  Google Scholar 

  3. Lai, J., Ford, J.J.: Relative Entropy Rate Based Multiple Hidden Markov Model Approximation. IEEE Transactions on Signal Processing 58, 165–174 (2010)

    Article  MathSciNet  Google Scholar 

  4. Mahler, R.: Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood (2007)

    MATH  Google Scholar 

  5. Goutsias, J., Mahler, R., Nguyen, H.: Random Sets Theory and Applications. Wiley, New York (2003)

    Google Scholar 

  6. Mahler, R.P.S.: Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems 39, 1152–1178 (2003)

    Article  Google Scholar 

  7. Mahler, R.: PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems 43, 1523–1543 (2007)

    Google Scholar 

  8. Ba-Ngu, V., Wing-Kin, M.: The Gaussian Mixture Probability Hypothesis Density Filter. IEEE Transactions on Signal Processing 54, 4091–4104 (2006)

    Article  Google Scholar 

  9. Ba-Ngu, V., Singh, S., Doucet, A.: Random finite sets and sequential Monte Carlo methods in multi-target tracking. In: Proceedings of the International Radar Conference, pp. 486–491 (2003)

    Google Scholar 

  10. Ba-Ngu, V., Ba-Tuong, V., Sumeetpal, S.: Sequential Monte Carlo methods for static parameter estimation in random set models. In: Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, pp. 313–318 (2004)

    Google Scholar 

  11. Yu, Z., Weijun, H., Jun, Z., Feng, D., Jinqiu, S., Jiang, L.: A New Starry Images Matching Method in Dim and Small Space Target Detection. In: Fifth International Conference on Image and Graphics, ICIG 2009, pp. 447–450 (2009)

    Google Scholar 

  12. Wing-Kin, M., Ba-Ngu, V., Singh, S.S., Baddeley, A.: Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach. IEEE Transactions on Signal Processing 54, 3291–3304 (2006)

    Article  Google Scholar 

  13. Sidenbladh, H., Wirkander, S.-L.: Tracking Random Sets of Vehicles in Terrain. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003, vol. 9, p. 98 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, L., Sun, J., Zhu, Y., Li, H. (2012). Dim Target Tracking Base on GM-PHD Filter. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31919-8_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31918-1

  • Online ISBN: 978-3-642-31919-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics