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Cluster Computing

, Volume 22, Supplement 6, pp 13283–13291 | Cite as

Study on multiple targets tracking algorithm based on multiple sensors

  • Biao WangEmail author
  • Kelei Feng
  • Wenzhong Yang
  • Zhiyu Zhu
Article
  • 181 Downloads

Abstract

For the problem that traditional data association algorithms tend to coalesce neighboring tracks for multiple close targets tracking application in dense clutter, measurements adaptive censor (MAC) method to Set JPDA (SJPDA) algorithm was introduced in this paper, then the proposed the MACSJPDA algorithm of target tracking discards several data associations with small probability and accelerates the convergence speed of the SJPDA algorithm. The algorithm can achieve better effects of multiple targets tracking by multiple sensors in wireless sensor networks. Monte Carlo simulation revealed that estimation effect of the MACSJPDA algorithm is much smoother, and it needs less run time than SJPDA algorithm for handling closely spaced and crossing targets, in the meanwhile the mean optimal sub-pattern assignment (MOSPA) deviation of the MACSJPDA algorithm is also smaller.

Keywords

JPDA Multiple targets tracking Sensor networks Target tracking 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (11574120, U1636117), the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing, Ministry of Education, China (UASP1503), the Natural Science Foundation of Jiangsu Province of China (BK20161359). Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project.

References

  1. 1.
    Obeid, A.M., Karray, F., Jmal, M.W., Abid, M., Qasim, S.M., BenSaleh, M.S.: Towards realisation of wireless sensor network-based water pipeline monitoring systems: a comprehensive review of techniques and platforms. IET Sci. Meas. Technol. 10(5), 420–426 (2016)CrossRefGoogle Scholar
  2. 2.
    Subedi, S., Zhang, Y.D., Amin, M.G., Himed, B.: Group sparsity based multi-target tracking in passive multi-static radar systems using doppler-only measurements. IEEE Trans. Signal Process. 64(14), 3619–3634 (2016)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Demigha, O., Hidouci, W.K., Ahmed, T.: On energy efficiency in collaborative target tracking in wireless sensor network: a review. IEEE Commun. Surv. Tutor. 15(3), 1210–1222 (2013)CrossRefGoogle Scholar
  4. 4.
    Mao, X., Tang, S., Wang, J., Li, X.Y.: iLight: device-free passive tracking using wireless sensor networks. IEEE Sens. J. 13(10), 3785–3792 (2013)CrossRefGoogle Scholar
  5. 5.
    Wu, P., Li, X., Zhang, L., Bo, Y.: Tracking algorithm with radar and infrared sensors using a novel adaptive grid interacting multiple model. IET Sci. Meas. Technol. 8(5), 270–276 (2014)CrossRefGoogle Scholar
  6. 6.
    Zhu, Y., Vikram, A., Fu, H.: On topology of sensor networks deployed for multitarget tracking. IEEE Trans. Intell Transp. Syst. 15(4), 1489–1498 (2014)CrossRefGoogle Scholar
  7. 7.
    Tang, X., Tharmarasa, R., McDonald, M., Kirubarajan, T.: Multiple detection-aided low-observable track initialization using ML-PDA. IEEE Trans. Aerosp. Electron. Syst. 53(2), 722–735 (2017)CrossRefGoogle Scholar
  8. 8.
    Svensson, D., Ulmke, M., Hammarstrand, L.: Multitarget sensor resolution model and joint probabilistic data association. IEEE Trans. Aerosp. Electron. Syst. 48(4), 3418–3434 (2012)CrossRefGoogle Scholar
  9. 9.
    Jian, K., Li, Y., Lin, Y., et al.: Joint probability data association algorithm based evidence theory. Syst. Eng. Electron. 35(8), 1620–1626 (2013)Google Scholar
  10. 10.
    Ebenezer, S.P., Papandreou-Suppappola, A.: Generalized recursive track-before-detect with proposal partitioning for tracking varying number of multiple targets in low SNR. IEEE Trans. Signal Process. 64(11), 2819–2834 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Svensson, L., Svensson, D., Guerriero, M., et al.: Set JPDA filter for multitarget tracking. IEEE Trans. Signal Process. 59(10), 4677–4691 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Granström, K., Willett, P., Bar-Shalom, Y.: Approximate multi-hypothesis multi-bernoulli multi-object filtering made multi-easy. IEEE Trans. Signal Process. 64(7), 1784–1797 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Williams, J.L.: An efficient, variational approximation of the best fitting multi-bernoulli filter. IEEE Trans. Signal Process. 63(1), 258–273 (2015)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kennedy, H.L.: Powerful test statistic for track management in clutter. IEEE Trans. Aerosp. Electron. Syst. 50(1), 207–223 (2014)CrossRefGoogle Scholar
  15. 15.
    Yan, J., Liu, H., Pu, W., Jiu, B., Liu, Z., Bao, Z.: Benefit analysis of data fusion for target tracking in multiple radar system. IEEE Sens. J. 16(16), 6359–6366 (2016)CrossRefGoogle Scholar
  16. 16.
    Selvan, R., Svensson, L.: A batch algorithm for estimating trajectories of point targets using expectation maximization. IEEE Trans. Signal Process. 64(18), 4792–4804 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Chen, Y., Zhao, Q., An, Z., Lv, P., Zhao, L.: Distributed multi-target tracking based on the K-MTSCF algorithm in camera networks. IEEE Sens. J. 16(13), 5481–5490 (2016)CrossRefGoogle Scholar
  18. 18.
    Habtemariam, B., Tharmarasa, R., Mcdonald, M., Kirubarajan, T.: Continuous 2-D assignment for multitarget tracking with rotating radars. IEEE Trans. Aerosp. Electron. Syst. 51(3), 2193–2204 (2015)CrossRefGoogle Scholar
  19. 19.
    Bandiera, F., Del Coco, M., Ricci, G.: Multitarget range-azimuth tracker. IEEE Trans. Aerosp. Electron. Syst. 51(2), 1515–1529 (2015)CrossRefGoogle Scholar
  20. 20.
    Tian, K., Zhang, F.: Multi-target tracking algorithm of boost-phase ballistic missile defense. J. Syst. Eng. Electron. 24(1), 90–100 (2013)CrossRefGoogle Scholar
  21. 21.
    Chen, Y., Jilkov, V.P., Li, X.R.: Multilane-road target tracking using radar and image sensors. IEEE Trans. Aerosp. Electron. Syst. 51(1), 65–80 (2015)CrossRefGoogle Scholar
  22. 22.
    Gulmezoglu, B., Guldogan, M.B.: Multiperson tracking with a network of ultrawideband radar sensors based on Gaussian mixture PHD filters. IEEE Sens. J. 15(4), 2227–2237 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Electronics and InformationJiangsu University of Science and TechnologyZhenjiangChina

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