Skip to main content
SpringerLink
Log in

Study on multiple targets tracking algorithm based on multiple sensors

  • Published:
Cluster Computing Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  11. Svensson, L., Svensson, D., Guerriero, M., et al.: Set JPDA filter for multitarget tracking. IEEE Trans. Signal Process. 59(10), 4677–4691 (2011)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  13. Williams, J.L.: An efficient, variational approximation of the best fitting multi-bernoulli filter. IEEE Trans. Signal Process. 63(1), 258–273 (2015)

    Article  MathSciNet  Google Scholar 

  14. Kennedy, H.L.: Powerful test statistic for track management in clutter. IEEE Trans. Aerosp. Electron. Syst. 50(1), 207–223 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Bandiera, F., Del Coco, M., Ricci, G.: Multitarget range-azimuth tracker. IEEE Trans. Aerosp. Electron. Syst. 51(2), 1515–1529 (2015)

    Article  Google Scholar 

  20. Tian, K., Zhang, F.: Multi-target tracking algorithm of boost-phase ballistic missile defense. J. Syst. Eng. Electron. 24(1), 90–100 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. College of Electronics and Information, Jiangsu University of Science and Technology, Mengxi Road No. 2, Zhenjiang, 212003, China

    Biao Wang, Kelei Feng, Wenzhong Yang & Zhiyu Zhu

Authors
  1. Biao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kelei Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biao Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, B., Feng, K., Yang, W. et al. Study on multiple targets tracking algorithm based on multiple sensors. Cluster Comput 22 (Suppl 6), 13283–13291 (2019). https://doi.org/10.1007/s10586-018-1846-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1846-3

Keywords

Search

Navigation