Skip to main content
SpringerLink
Log in

A cross-and-dot-product neural network based filtering for maneuvering-target tracking

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Maneuvering-target tracking is a critical topic for target tracking. However, suffering from unknown and changeable target movements, tracking performance of common algorithms still leaves large rooms for improvement. Recently, neural network-based algorithms are proposed to greatly improve the tracking performance, while their generalization ability remains a problem. That is, when the target states distribute beyond the range of training set, their tracking errors will dramatically increase. To conquer this problem, this paper proposes a Cross-and-Dot-Product neural network based filtering algorithm. Specifically, a network is designed to estimate transition matrices with turn rate information of moving targets by calculating the cross-product and dot-product data. Thereby, our network can correctly understand the maneuvering-target movements as real-time constant turn models, instead of a database-limited end-to-end mapping. Furthermore, an adaptive probability allocation method is designed to form a double-channel filtering algorithm. The proposed algorithm derives the final tracking results by fusing the states from two unscented Kalman filters together: one filter is based on the constant velocity model, and the other is based on the model with the transition matrices estimated by network. The simulation results verify that the proposed algorithm outperforms other state-of-the-art algorithms both in tracking performance and generalization ability.

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
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45

    Article  MathSciNet  Google Scholar 

  2. Julier SJ, Uhlmann JK (1999) A new extension of the kalman filter to nonlinear systems. Proc SPIE Int Soc Opt Eng 3068:182–193

    Google Scholar 

  3. Julier S, Uhlmann J, Durrantwhyte HF (2000) A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans Autom Control 45(3):477–482

    Article  MathSciNet  Google Scholar 

  4. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  5. Blom HA, Bar-Shalom Y (1988) The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans Autom Control 33(8):780–783

    Article  Google Scholar 

  6. Daeipour E, Bar-Shalom Y (1998) Imm tracking of maneuvering targets in the presence of glint. IEEE Trans Aerosp Electron Syst 34(3):996–1003

    Article  Google Scholar 

  7. Li W, Jia Y, Du J, Yu F (2011) Gaussian mixture phd smoother for jump markov models in multiple maneuvering targets tracking. In: Proceedings of the 2011 American Control Conference, pp. 3024–3029. https://doi.org/10.1109/ACC.2011.5991161

  8. Gao L, Xing J, Ma Z, Sha J, Meng X (2012) Improved imm algorithm for nonlinear maneuvering target tracking. Proc Eng 29:4117–4123

    Article  Google Scholar 

  9. Petridis V, Kehagias A (1998) A multi-model algorithm for parameter estimation of time-varying nonlinear systems! Automatica 34(4):469–475

    Article  Google Scholar 

  10. Li XR, Zhao Z, Li XB (2005) General model-set design methods for multiple-model approach. IEEE Trans Autom Control 50(9):1260–1276

    Article  MathSciNet  Google Scholar 

  11. Li XR, Bar-Shalom Y (1996) Multiple-model estimation with variable structure. IEEE Trans Autom Control 41(4):478–493

    Article  MathSciNet  Google Scholar 

  12. Li XR (2000) Multiple-model estimation with variable structure. II. Model-set adaptation. IEEE Trans Autom Control 45(11):2047–2060. https://doi.org/10.1109/9.887626

    Article  MathSciNet  MATH  Google Scholar 

  13. Xu L, Li XR, Duan Z (2016) Hybrid grid multiple-model estimation with application to maneuvering target tracking. IEEE Trans Aerosp Electron Syst 52(1):122–136. https://doi.org/10.1109/TAES.2015.140423

    Article  Google Scholar 

  14. Sheng H, Zhao W, Wang J (2017) Interacting multiple model tracking algorithm fusing input estimation and best linear unbiased estimation filter. IET Radar Sonar Navig 11(1):70–77

    Article  Google Scholar 

  15. Li T, Corchado JM, Chen H, Bajo J (2017) Track a smoothly maneuvering target based on trajectory estimation. In: Proceedings of the 2017 20th international conference on information fusion (Fusion), pp. 1–8. https://doi.org/10.23919/ICIF.2017.8009731

  16. Li T, Chen H, Sun S, Corchado Rodräguez J (2017) Joint smoothing, tracking, and forecasting based on continuous-time target trajectory fitting. arXiv:abs/1708.02196

  17. Liu J, Wang Z, Xu M (2020) Deepmtt: a deep learning maneuvering target-tracking algorithm based on bidirectional lstm network. Inform Fusion 53:289–304

    Article  Google Scholar 

  18. Shojaei K (2019) Three-dimensional neural network tracking control of a moving target by underactuated autonomous underwater vehicles. Neural Comput Appl 31(2):509–521

    Article  Google Scholar 

  19. Gao C, Yan J, Zhou S, Varshney PK, Liu H (2019) Long short-term memory-based deep recurrent neural networks for target tracking. Inf Sci 502:279–296

    Article  Google Scholar 

  20. Kozy M, Yu J, Buehrer RM, Martone A, Sherbondy K (2019) Applying deep-q networks to target tracking to improve cognitive radar. In: Proceedings of the 2019 IEEE Radar Conference (RadarConf19)

  21. Liu H, Xia L, Wang C (2019) Maneuvering target tracking using simultaneous optimization and feedback learning algorithm based on elman neural network. Sens (Basel Switzerl) 19(7)

  22. Song L, Shengli W, Dingbao X (2019) Radar track prediction method based on bp neural network. J Eng 2019(21):8051–8055

    Article  Google Scholar 

  23. Wang Y, Wang Q, Suo D, Wang T (2021) Intelligent traffic monitoring and traffic diagnosis analysis based on neural network algorithm. Neural Comput Appl 33(14):8107–8117

    Article  Google Scholar 

  24. Li G, Peng M, Nai K, Li Z, Li K (2020) Multi-view correlation tracking with adaptive memory-improved update model. Neural Comput Appl 32(13):9047–9063

    Article  Google Scholar 

  25. Li XR, Jilkov VP (2003) Survey of maneuvering target tracking. Part I. Dynamic models. IEEE Trans Aerosp Electron Syst 39(4):1333–1364

    Article  Google Scholar 

  26. Li XR, Bar-Shalom Y (1993) Design of an interacting multiple model algorithm for air traffic control tracking. IEEE Trans Control Syst Technol 1(3):186–194

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by NSFC under Grant Number 62061003 and Natural Science Foundation of Guangxi Provence under Grant Numbers AD19245047 and 2019GXNSFAA245049, in part by Guangxi University of Science and Technology Doctoral Fund, 62062010 and 62166002, in part by Natural Science Foundation of Guangxi Provence under Grant Numbers 2018GXNSFAA050020 and 2019GXNSFAA245033.

Author information

Authors and Affiliations

  1. School of Electrical Electronic and Computer Science, Guangxi University of Science and Technology, 268 Donghuan Avenue, Liuzhou, Guangxi, 545006, China

    Jingxian Liu, Shuhong Yang & Fan Yang

Authors
  1. Jingxian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuhong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuhong Yang.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the manuscript entitled “A Cross-and-Dot-Product Neural Network based Filtering for Maneuvering-Target Tracking.”

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Yang, S. & Yang, F. A cross-and-dot-product neural network based filtering for maneuvering-target tracking. Neural Comput & Applic 34, 14929–14944 (2022). https://doi.org/10.1007/s00521-022-07338-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07338-7

Keywords

Search

Navigation