Multi-Level Fusion Processing Algorithm for Complex Radar Signals Based on Evidence Theory

  • Tian, Runlan (Dept. of information countermeasures, Aviation University of Air Force) ;
  • Zhao, Rupeng (Dept. of information countermeasures, Aviation University of Air Force) ;
  • Wang, Xiaofeng (Dept. of information countermeasures, Aviation University of Air Force)
  • Received : 2017.03.29
  • Accepted : 2017.12.12
  • Published : 2019.10.31


As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.


Complex Radar Signal;Evidence Theory;Multi-Level Fusion;Similarity


Supported by : China National Natural Science Foundation


  1. Y. Shen, Y. Chen, and X. Li, "Multi-data fusion algorithm for radar emitter identification," Journal of Electronics & Information Technology, vol. 29, no. 10, pp. 2329-2332, 2007.
  2. H. Li, K. Zheng, W. Jin, W. Xiong, and T. Chen, "Working mode identification of airborne phased-array radar based on multi-level modeling," Electronic Warfare Technology, vol. 31, no. 4, pp. 1-5, 2016.
  3. C. B. Fu, J. Zhang, W. P. Ji, and Y. I. Zhang, "Research on the application of gray correlation theory on multi-sensor radiation recognition system," Journal of China Academy of Electronics and Information Technology, vol. 10, no 6, pp. 602-606, 2015.
  4. K. Liu, J. G. Wang, and J. W. Li, "A new method based on interval Grey association for radar emitter recognition," Fire Control & Command Control, vol. 38, no. 7, pp. 20-23, 2013.
  5. Z. Q. Wu, S. Chang, and G. Zhang, "The cloud model and the improved grey relational algorithm applied in radar emitter recognition," Electronic Warfare Technology, vol. 30, no. 4, pp. 21-24, 2015.
  6. F. Deng and Q. Jiang, "Emitter recognition of multi-sensor data fusion based on fuzzy D-S evidence theory," Electronics Optics & Control, vol. 15, no. 4, pp. 34-38, 2008.
  7. Z. Li, C. Qu, F. Su, and D. Ping, "Improved radar emitter fuzzy identification algorithm," Journal of University of Electronic Science and Technology of China, vol. 39, no. 2, pp. 182-185, 2010.
  8. J. Wang, J. Luo, and C. Yin, "Multisensor data fusion for airborne emitter identification," Signal Processing, vol. 18, no. 1, pp. 12-15, 2002.
  9. F. Wang, Z. Guo, J. Huang, and Z. Pei, "Recognition model for radar emitter identification with multisensor based on D-S evidence theory," Modern Defence Technology, vol. 38, no. 1, pp. 60-63, 2010.
  10. M. E. Y. Boudaren, E. Monfrini, W. Pieczynski, and A. Aissani, "Dempster-Shafer fusion of multisensor signals in nonstationary Markovian context," EURASIP Journal on Advances in Signal Processing, vol. 2012, article no. 134, 2012.
  11. H. Liu, Z. Liu, W. Jiang, and Y. Zhou, "A joint-parameter based radar emitter identification method," Journal of Astronautics, vol. 32, no. 1, pp. 142-149, 2011.
  12. X. Su, S. Mahadevan, P. Xu, and Y. Deng, "Handling of dependence in Dempster-Shafer theory," International Journal of Intelligent Systems, vol. 30, no. 4, pp. 441-467, 2015.
  13. X. Su, W. Han, P. Xu, and Y Deng, "Review of combining dependent evidence," Systems Engineering and Electronics, vol. 38, no. 6, pp. 1345-1351, 2016.
  14. S. Zhao and J. Zhou, "A fault-tolerant detection fusion strategy for distributed multisensor systems," International Journal of Distributed Sensor Networks, vol. 12, no. 2, article no. 8613149, 2016.
  15. Z. Wang, W. Hu, W. Yu, and Z. Zhuang, "A fast evidential combination method based on truncated Dempster-Shafer," Journal of Electronics and Information Technology, vol. 24, no. 12, pp. 1863-1869, 2002.