Radar Burst Control Based on Constrained Ordinal Optimization Under Guidance Quality Constraints

  • Bo Li
  • Qingying Li
  • Daqing Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 237)


Radar burst control has come into use in order to improve the survivability of combat aircraft and ensure operational effectiveness in the increasingly harsh electronic warfare environment. The critical factor in radar burst control is the radar burst timing. In this paper, a novel method is proposed to determine the optimal timing based on constrained ordinal optimization. Taking the combat effectiveness of air-to-air missile as the constraint condition, the constrained ordinal optimization method is applied to the radar burst detection of hybrid control. The optimal burst timing can be selected quickly and efficiently while making the combat effectiveness maximized. Simulation results indicate that the proposed method can significantly improve the searching efficiency of the optimal radar burst timing.


Radar optimal burst timing Hybrid control Constrained ordinal optimization Operational effectiveness 



This research was supported by The Fundamental Research Funds for the Central Universities under grant No. 3102016CG002.


  1. 1.
    Xue-quan, L., Bo, L., Kai-fang, W., et al.: Study on radar burst technology based on multi-sensor synergy. J. Civ. Aviat. Univ. China 30(6), 17–20 (2012)Google Scholar
  2. 2.
    Vo, B.T., See, C.M., Ma, N., et al.: Multi-sensor joint detection and tracking with the Bernoulli filter. IEEE Trans. Aerosp. Electron. Syst. 48(2), 1385–1402 (2012)CrossRefGoogle Scholar
  3. 3.
    Frey, T.: Cooperative sensor resource management for improved situation awareness. In: Infotech@ Aerospace 2012 (2012)Google Scholar
  4. 4.
    Toupet, O., How, J.: Collaborative sensor fusion and management for multiple UAVs. In: AIAA Infotech@ Aerospace Conference (2011)Google Scholar
  5. 5.
    Deng, R., Chen, J., Yuen, C., et al.: Energy-efficient cooperative spectrum sensing by optimal scheduling in sensor-aided cognitive radio networks. IEEE Trans. Veh. Technol. 61(2), 716–725 (2012)CrossRefGoogle Scholar
  6. 6.
    Kenefic, R.J.: Sensor rotation bias removal for multiple hypothesis tracking applications. J. Aerosp. Inf. Syst. 10(5), 250–257 (2013)Google Scholar
  7. 7.
    Wei, W., Yi, L., Guo-hong, W., et al.: Active and passive synergy tracking technique with emission constraint. Inf. Control 40(3), 418–423 (2011)Google Scholar
  8. 8.
    Ho, Y.-C., Zhao, Q.-C., Jia, Q.-S.: Ordinal Optimization: Soft Optimization for Hard Problems. Springer, New York (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Liu, X.-Q., Li, B., Wan, K.-F., et al.: Analysis and application of AN/ APG-77 radar remote guidance search mode. In: The Proceedings of the Academic Annual Meeting of Fire and Command Control in 2013, 11 (2013)Google Scholar
  10. 10.
    Lau, T.W.E., Ho, Y.C.: Universal alignment probabilities and subset selection for ordinal optimization. J. Optim. Theory Appl. 93(3), 455–489 (1997)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.School of Information and ElectronicsNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of EngineeringLondon South Bank UniversityLondonEngland

Personalised recommendations