Abstract
Due to the coexistence of multiple electromagnetic interference, the operational performance of radar equipment will be seriously affected. Therefore, it is necessary to study the anti-jamming problem of airborne radar. In view of the problem that airborne radar is easily affected by point source signal interference under the traditional method, an airborne radar anti-jamming method based on artificial intelligence is proposed. The anti-jamming method is designed. Firstly, the airborne radar is detected by frequency shift, and the detected information is analyzed to judge the jamming environment and identify the point source target intelligently. Then the suppression jamming filter is generated based on the analysis of the point source jamming information, and then the suppression jamming signal is output. Finally, the anti-jamming method of airborne radar is obtained. The performance results of the airborne radar anti-point source jamming method are analyzed by simulation experiments. Compared with traditional method, the proposed anti-jamming method can effectively suppress the point source jamming information, the radar signal is clearer and the anti-jamming effect is better. The results verify the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shi, C., Wang, F., Sellathurai, M., et al.: Low probability of intercept-based distributed MIMO radar waveform design against barrage jamming in signal-dependent clutter and colored noise. IET Signal Process. 13(4), 415–423 (2019)
Shen, W., Xu, F., Wu, G.X., et al.: A multi-station angle fusion anti-jamming method. Modern Radar, 22(1), 47–50 (2018)
Tao, Z., Shaoqiang, C., Huayu, F., et al.: Design and processing of a novel chaos-based stepped frequency synthesized wideband radar signal. Sensors 18(4), 985 (2018)
Lee, G.H., Jo, J., Park, C.H.: Jamming prediction for radar signals using machine learning methods. Secur. Commun. Netwk. 2020(3), 1–9 (2020)
Wen, C., Peng, J., Zhou, Y., et al.: Enhanced three-dimensional joint domain localized stap for airborne fda-mimo radar under dense false-target jamming scenario. IEEE Sensors J. 18(10), 4154–4166 (2018)
Paine, S., O’Hagan, D.W., Inggs, M., et al.: Evaluating the performance of fm-based pcl radar in the presence of jamming. IEEE Trans. Aerospace Electronic Syst. 55(2), 631–643 (2019)
Saeedi, J.: A new hybrid method for synthetic aperture radar deceptive jamming. Int. J. Microwave Wireless Technol. 4(1), 1–14 (2019)
Hanbali, S.B.S.: A review of radar signals in terms of Doppler tolerance, time-sidelobe level, and immunity against jamming. Int. J. Microwave Wireless Technol. 10(10), 1–9 (2018)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mobile Netwk. Appl. 24(1), 1–4 (2019)
Liu, S., Glowatz, M., Zappatore, M., et al.: e-Learning, e-Education, and Online Training. Springer International Publishing, Berlin (2018)
Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy 21(9), 902 (2019)
Shi, C.G., Wang, F., Salous, S. et al.: Adaptive jamming waveform design for distributed multiple-radar architectures based on low probability of intercept. Radio Sci. 54(1–2), 72–90 (2019)
Enzheng, Z., Benyong, C., Hao, Z., et al.: Laser heterodyne interference signal processing method based on phase shift of reference signal. Opt. Express 26(7), 8656 (2018)
Lu, M., Liu, S.: Nucleosome positioning based on generalized relative entropy. Soft. Comput. 23(19), 9175–9188 (2018). https://doi.org/10.1007/s00500-018-3602-2
Joshi, H.D., Kaur, R., Singh, A.K., et al.: An improved method for deceptive jamming against synthetic aperture radar. Int. J. Microwave Wireless Technol. 10(1), 115–121 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, Za., Lu, Jg., Dong, Z., Jie, Yh. (2021). Research on Anti-point Source Jamming Method of Airborne Radar Based on Artificial Intelligence. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67874-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-67874-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67873-9
Online ISBN: 978-3-030-67874-6
eBook Packages: Computer ScienceComputer Science (R0)