Skip to main content

Advertisement

SpringerLink
Log in

Anti-interference recognition method of aerial infrared targets based on the Bayesian network

  • Research Article
  • Published:
Journal of Optics Aims and scope Submit manuscript

Abstract

The development of infrared decoy countermeasure technologies has resulted in the complexity of the air combat environment. Therefore, higher requirements are put forward considering the infrared imaging-guided air-to-air missile anti-interference target recognition technology. The release of infrared decoys destroys the completeness, saliency and stability of target features. It is difficult for statistical pattern recognition methods based on feature fusion and matching to cover all of the confrontation conditions. In the present study, a Bayesian probabilistic recognition model is proposed that deals with uncertain information about targets and decoys. The proposed model deals with the interference of the air battlefield with the confrontation environment. Moreover, it simulates some functions of the human visual cognition and improves the target recognition ability when interference exists. Furthermore, numerous simulation data samples are utilized to solve the network structure as well as parameters of the Bayesian recognition model and the probabilistic definitions of the target and interference are achieved. Finally, a new aerial infrared target recognition algorithm is constructed based on prior information and the probability recognition model. Results of simulation experiments indicate that the recognition rate of the anti-interference recognition algorithm based on the Bayesian network reaches 90.73%, which is 6.905% higher than that of the anti-interference recognition algorithm based on Naive Bayes classifier in the tested air combat anti-interference simulation image dataset. Moreover, it can solve the problem of anti-interference target recognition to a certain extent such as false targets and target occlusion.

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. M. Atoui, A. Cohen, S. Verron, A. Kobi, A single Bayesian network classifier for monitoring with unknown classes. Eng. Appl. Artif. Intell. 85, 681–690 (2019). https://doi.org/10.1016/j.engappai.2019.07.016

    Article  Google Scholar 

  2. A. Bocanegra-Valle, Input and interlanguage: a review of the research. Estudios Ingleses de la Universidad Complutense 1998(6), 129–159 (1998)

    Google Scholar 

  3. V. Cherkassky, The nature of statistical learning theory. Trans. Neural Netw. 8(6), 1564 (1997). https://doi.org/10.1109/TNN.1997.641482

    Article  Google Scholar 

  4. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1023/A:1022627411411

    Article  MATH  Google Scholar 

  5. Z. Fan, D. Bi, S. Gao, L. He, W. Ding, Adaptive enhancement for infrared image using shearlet frame. J. Opt. 18, 085–706 (2016). https://doi.org/10.1088/2040-8978/18/8/085706

    Article  Google Scholar 

  6. Y. Fu, D. Ma, H. Zhang, L., Zheng, Moving object recognition based on SVM and binary decision tree, in 2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS) (2017), pp. 495–500. https://doi.org/10.1109/DDCLS.2017.8068122

  7. D. Geiger, G. Provan, P. Langley, P. Smyth, Bayesian network classifiers. Mach. Learn. 29, 131–163 (2000). https://doi.org/10.1023/A:1007465528199

    Article  Google Scholar 

  8. Y. He, D. Liu, S. Yi, A novel endmember extraction and discrimination algorithm for target detection in hyperspectral imagery. J. Opt. (2011). https://doi.org/10.1088/2040-8978/13/8/085402

    Article  Google Scholar 

  9. Z. Huang, Y. Zhang, Q. Li, Z. Li, T. Zhang, N. Sang, S. Xiong, Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images. Int. J. Remote Sens. 40(15), 5737–5748 (2009). https://doi.org/10.1080/01431161.2019.1580821

    Article  Google Scholar 

  10. Z. Huang, L. Huang, Q. Li, T. Zhang, N. Sang, Framelet regularization for uneven intensity correction of color images with illumination and reflectance estimation. Neurocomputing 314, 154–168 (2018)

    Article  Google Scholar 

  11. Z. Huang, L. Chen, Y. Zhang, Z. Yu, T. Zhang, Robust contact-point detection from pantograph-catenary infrared images by employing horizontal-vertical enhancement operator. Infrared Physics & Technology 101, 146–155 (2019)

    Article  ADS  Google Scholar 

  12. Z. Huang, Y. Zhang, X. Yue, X. Li, H. Fang, H. Hong, T. Zhang, Joint horizontal-vertical enhancement and tracking scheme for robust contact-point detection from pantograph-catenary infrared images. Infrared Phys. Technol. (2020). https://doi.org/10.1016/j.infrared.2019.103156

    Article  Google Scholar 

  13. D. Hubel, T. Wiesel, R. Fields, Binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–54 (1962). https://doi.org/10.1113/jphysiol.1962.sp006837

    Article  Google Scholar 

  14. X. Jia, J. Yang, R. Liu, X. Wang, S.D. Cotofana, W. Zhao, Efficient computation reduction in Bayesian neural networks through feature decomposition and memorization. IEEE Trans. Neural Netw. Learn. Syst. (2020). https://doi.org/10.1109/TNNLS.2020.2987760

    Article  Google Scholar 

  15. M. Kukar, I. Kononenko, T. Silvester, Machine learning in prognosis of the femoral neck fracture recovery. Artif. Intell. Med. 8(5), 431–451 (1996). https://doi.org/10.1016/S0933-3657(96)00351-X

    Article  Google Scholar 

  16. G. Labonte, W. Deck, Infrared target-flare discrimination using a ZISC hardware neural network. J. Real-Time Image Process. (2010). https://doi.org/10.1007/s11554-009-0121-5

    Article  Google Scholar 

  17. Y. Li, D.K. Jha, A. Ray, T.A. Wettergren, Information fusion of passive sensors for detection of moving targets in dynamic environments. IEEE Trans. Cybern. 47(1), 93–104 (2017). https://doi.org/10.1109/TCYB.2015.2508024

    Article  Google Scholar 

  18. G. Marsiglia, L. Fortunato, A. Ondini, G. Balzarotti, Template matching techniques for automatic IR target recognition in real and simulated scenarios: tests and evaluations. Proc. SPIE (2003). https://doi.org/10.1117/12.486065

    Article  Google Scholar 

  19. D. Nameirakpam, M. Khumanthem, C.Y. Jina, Image segmentation using k -means clustering algorithm and subtractive clustering algorithm. Proc. Comput. Sci. 54, 764–771 (2015). https://doi.org/10.1016/j.procs.2015.06.090. Eleventh International Conference on Communication Networks, ICCN, August 21-23, 2015, Bangalore, India Eleventh International Conference on Data Mining and Warehousing, ICDMW 2015, August 21-23, 2015, Bangalore, India Eleventh International Conference on Image and Signal Processing, ICISP 2015, August 21-23, 2015 (Bangalore, India, 2015)

    Article  Google Scholar 

  20. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: unified, real-time object detection, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), pp. 779–788. https://doi.org/10.1109/CVPR.2016.91

  21. S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  22. H. Schneiderman, Learning a restricted Bayesian network for object detection, in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 2 (2004), pp. II–II. https://doi.org/10.1109/CVPR.2004.1315224

  23. M.A. Smeelen, P.B.W. Schwering, A. Toet, M. Loog, Semi-hidden target recognition in gated viewer images fused with thermal IR images. Inf. Fusion 18, 131–147 (2014). https://doi.org/10.1016/j.inffus.2013.08.001

    Article  Google Scholar 

  24. V. Vapnik, V. Vapnik, V.N. Vapnik, Statistical learning theory. Ann. Inst. Stat. Math. 55(2), 371–389 (2003)

    MathSciNet  MATH  Google Scholar 

  25. T. Yuan, X. Liu, S.Z. Ramadan, Y. Kuo, Bayesian analysis for accelerated life tests using a Dirichlet process Weibull mixture model. IEEE Trans. Reliab. 63(1), 58–67 (2014). https://doi.org/10.1109/TR.2014.2299675

    Article  Google Scholar 

  26. J. Yuan, Z. Wang, Y. Sun, W. Zhang, J. Jiang, An effective pattern-based Bayesian classifier for evolving data stream. Neurocomputing 295, 17–28 (2018). https://doi.org/10.1016/j.neucom.2018.01.016

    Article  Google Scholar 

  27. Z. Zou, Z. Shi, Y., Guo, J. Ye, Object detection in 20 years: a survey. CoRR (2019). arxiv:1905.05055

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 61703337), and by Aviation Science Foundation of China (Grant No. ASFC-20191053002).

Author information

Authors and Affiliations

  1. School of Astronautics, Northwestern Polytechnical University, No.127 Youyi West Road, 710072, Xi’an, China

    Shaoyi Li, Xiaoqian Tian & Xi Yang

  2. Xi’an Electronic Engineering Research Institute, No.9 East Fengxi Road, 710100, Xi’an, China

    Kai Yang

  3. Xi’an Modern Control Technology Research Institute, No.10 Zhangba East Road, 710065, Xi’an, China

    Jun Ma

Authors
  1. Shaoyi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoqian Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaoyi Li.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

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

Li, S., Yang, K., Ma, J. et al. Anti-interference recognition method of aerial infrared targets based on the Bayesian network. J Opt 50, 264–277 (2021). https://doi.org/10.1007/s12596-021-00701-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12596-021-00701-2

Keywords

Search

Navigation