Skip to main content
SpringerLink
Log in

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

  • Published:
Journal of Zhejiang University SCIENCE C Aims and scope Submit manuscript

Abstract

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli (SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

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

Similar content being viewed by others

References

  • Bar-Shalom, Y., Li, X.R., Kirubarajan, T., 2001. Estimation with Applications to Tracking and Navigation. Wiley, New York.

    Book  Google Scholar 

  • Baser, E., Kirubarajan, T., Efe, M., 2013. Improved MeMBer filter with modeling of spurious targets. Int. Conf. on Information Fusion, p.813–819.

    Google Scholar 

  • Blackman, S., Popoli, R., 1999. Design and Analysis of Modern Tracking Systems. Artech House, Norwood.

    MATH  Google Scholar 

  • Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A., 2013a. Multi-Bernoulli sensor control for multi-target tracking. Proc. IEEE 8th Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, p.312–317. [doi:10.1109/ISSNIP.2013.6529808]

    Google Scholar 

  • Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A., 2013b. Robust multi-Bernoulli sensor selection for multi-target tracking in sensor networks. IEEE Trans. Signal Process. Lett., 20(12):1167–1170. [doi:10.1109/LSP.2013.2283735]

    Article  Google Scholar 

  • Hoang, H.G., Vo, B.T., 2014. Sensor management for multi-target tracking via multi-Bernoulli filtering. Automatica, 50(4):1135–1142. [doi:10.1016/j.automatica.2014.02.007]

    Article  MathSciNet  Google Scholar 

  • Hoseinnezhad, R., Vo, B.N., Vo, B.T., et al., 2011. Bayesian integration of audio and visual information for multi-target tracking using a CB-MeMBer filter. IEEE Int. Conf. on Acoust Speech Signal Processing, p.2300–2303. [doi:10.1109/ICASSP.2011.5946942]

    Google Scholar 

  • Hoseinnezhad, R., Vo, B.N., Vo, B.T., 2013. Visual tracking in background subtracted image sequences via multi-Bernoulli filtering. IEEE Trans. Signal Process., 61(2):392–397. [doi:10.1109/TSP.2012.2222389]

    Article  MathSciNet  Google Scholar 

  • Li, X.R., Jilkov, V., 2003. Survey of maneuvering target tracking. IEEE Trans. Aerosp. Electron. Syst., 39(4):1333–1364. [doi:10.1109/TAES.2003.1261132]

    Article  Google Scholar 

  • Lian, F., Li, C., Han, C.Z., et al., 2012. Convergence analysis for the SMC-MeMBer and SMCCBMeMBer filters. J. Appl. Math., Article ID 584140. [doi:10.1155/2012/584140]

    Google Scholar 

  • Mahler, R., 2003. Multitarget Bayes filtering via first-order multitarget moments. IEEE Trans. Aerosp. Electron. Syst., 39(4):1152–1178. [doi:10.1109/TAES.2003.1261119]

    Article  Google Scholar 

  • Mahler, R., 2007a. PHD filters of higher order in target number. IEEE Trans. Aerosp. Electron. Syst., 43(4):1523–1543. [doi:10.1109/TAES.2007.4441756]

    Article  Google Scholar 

  • Mahler, R., 2007b. Statistical Multisource-Multitarget Information Fusion. Artech House, Norwood.

    MATH  Google Scholar 

  • Ravindra, V.C., Svensson, L., Hammarstrand, L., et al., 2012. A cardinality preserving multitarget multi-Bernoulli RFS tracker. Int. Conf. on Information Fusion, p.832–839.

    Google Scholar 

  • Schuhmacher, D., Vo, B.T., Vo, B.N., 2008. A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process., 56(8):3447–3457. [doi:10.1109/TSP.2008.920469]

    Article  MathSciNet  Google Scholar 

  • Vo, B.N., Ma, W., 2006. The Gaussian mixture probability hypothesis density filter. IEEE Trans. Signal Process., 54(11):1091–4004. [doi:10.1109/TSP.2006.881190]

    Article  Google Scholar 

  • Vo, B.N., Singh, S., Doucet, A., 2005. Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Trans. Aerosp. Electron. Syst., 41(4):1224–1245. [doi:10.1109/TAES.2005.1561884]

    Article  Google Scholar 

  • Vo, B.N., Vo, B.T., Pham, N.T., et al., 2010. Joint detection and estimation of multiple objects from image observations. IEEE Trans. Signal Process., 58(10):5129–5141. [doi:10.1109/TSP.2010.2050482]

    Article  MathSciNet  Google Scholar 

  • Vo, B.T., 2008. Random Finite Sets in Multi-object Filtering. PhD Thesis, The University of Western Australia, Perth, Australia.

    Google Scholar 

  • Vo, B.T., Vo, B.N., 2013. Labeled random finite sets and multi-object conjugate priors. IEEE Trans. Signal Process., 61(13):3460–3475. [doi:10.1109/TSP.2013.2259822]

    Article  MathSciNet  Google Scholar 

  • Vo, B.T., Vo, B.N., Cantoni, A., 2007. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Trans. Signal Process., 55(7):3553–3567. [doi:10.1109/TSP.2007.894241]

    Article  MathSciNet  Google Scholar 

  • Vo, B.T., Vo, B.N., Cantoni, A., 2009. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Trans. Signal Process., 57(2):409–423. [doi:10.1109/TSP.2008.2007924]

    Article  MathSciNet  Google Scholar 

  • Vo, B.T., Vo, B.N., Hoseinnezhad, R., et al., 2011. Multi-Bernoulli filtering with unknown clutter intensity and sensor field-of-view. 45th Annual Conf. on Information Sciences and Systems, p.1–6. [doi:10.1109/CISS.2011.5766180]

    Google Scholar 

  • Vo, B.T., Vo, B.N., Hoseinnezhad, R., et al., 2013. Robust multi-Bernoulli filtering. IEEE J. Sel. Topics Signal Process., 7(3):399–409. [doi:10.1109/JSTSP.2013.2252325]

    Article  Google Scholar 

  • Wei, J., Zhang, X., 2009. Dynamic node collaboration for mobile multi-target tracking in two-tier wireless camera sensor networks. Proc. IEEE Military Communications Conf., p.1–7. [doi:10.1109/MILCOM.2009.5379919]

    Google Scholar 

  • Wei, J., Zhang, X., 2010a. Efficient node collaboration for mobile multi-target tracking using two-tier wireless camera sensor networks. IEEE Int. Conf. on Communications, p.1–5. [doi:10.1109/ICC.2010.5502158]

    Google Scholar 

  • Wei, J., Zhang, X., 2010b. Sensor self-organization for mobile multi-target tracking in decentralized wireless sensor networks. IEEE Wireless Communications and Networking Conf., p.1–6. [doi:10.1109/WCNC.2010.5506184]

    Google Scholar 

  • Williams, J.L., 2012. Hybrid Poisson and multi-Bernoulli filters. Int. Conf. on Information Fusion, p.1103–1110.

    Google Scholar 

  • Yin, J.J., Zhang, J.Q., 2010. The nonlinear multi-target multi-Bernoulli filter using polynomial interpolation. Proc. IEEE 10th Int. Conf. on Signal Processing, p.2551–2554. [doi:10.1109/ICOSP.2010.5656888]

    Chapter  Google Scholar 

  • Yin, J.J., Zhang, J.Q., Zhao, J., 2010. The Gaussian particle multi-target multi-Bernoulli filter. IEEE 2nd Int. Conf. on Advanced Computer Control, p.556–560. [doi:10.1109/ICACC.2010.5486859]

    Google Scholar 

  • Zheng, Y.M., Shi, Z.G., Lu, R.X., et al., 2013. An efficient data-driven particle PHD filter for multitarget tracking. IEEE Trans. Ind. Inform., 9(4):2318–2326. [doi:10.1109/TII.2012.2228875]

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Tong-yang Jiang & Mei-qin Liu

  2. College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China

    Tong-yang Jiang, Mei-qin Liu, Xie Wang & Sen-lin Zhang

Authors
  1. Tong-yang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mei-qin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sen-lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mei-qin Liu.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61174142, 61222310, and 61374021), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Nos. 20120101110115 and 20130101110109), the Zhejiang Provincial Science and Technology Planning Projects of China (No. 2012C21044), the Marine Interdisciplinary Research Guiding Funds for Zhejiang University (No. 2012HY009B), the Fundamental Research Funds for the Central Universities (No. 2014XZZX003-12), and the Aeronautical Science Foundation of China (No. 20132076002)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, Ty., Liu, Mq., Wang, X. et al. An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering. J. Zhejiang Univ. - Sci. C 15, 445–457 (2014). https://doi.org/10.1631/jzus.C1400025

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.C1400025

Key words

CLC number

Search

Navigation