Learning Algorithm for Tracking Hypersonic Targets in Near Space

  • Luyao CuiEmail author
  • Aijun Liu
  • Changjun Yv
  • Taifan Quan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 227)


With the development of hypersonic vehicles in near space such as X-51A, HTV-2 and so on, tracking for them is becoming a new task and hotspot. In this paper, a learning tracking algorithm is introduced for hypersonic targets, especially for the sliding jump maneuver. Firstly the algorithm uses the Sine model, which makes the tracking model more close to the particular maneuver, next two Sine models different in angular velocity are used into IMM algorithm, and it learns the target tracking error characteristics to adjust the sampling rate adaptively. The algorithm is compared with the single accurate model algorithm and general IMM algorithms with fixed sampling rate. Through simulation experiments it is proved that the algorithm in this paper can improve the tracking accuracy effectively.


Learn Target tracking Near space Interacting multiple models Sampling rate 



This work was supported by the National Natural Science Foundation of China (No. 61571159).


  1. 1.
    Chen, W., Wu, X., Tang, Y.: Application of thrust vectoring control technology in near space vehicle. Winged Missiles J. (5), 64–70 (2013)Google Scholar
  2. 2.
    Li, C., Bi, H., Zhang, B., Xiao, S.: An improved tracking algorithm for hypersonic targets. J. Air Force Eng. Univ. (Nat. Sci. Edn.) 13(5), 50–54 (2012)Google Scholar
  3. 3.
    Qin, L., Li, J., Zhou, D.: Tracking for near space target based on IMM algorithm. Syst. Eng. Electron. 36(7), 1243–1249 (2014)Google Scholar
  4. 4.
    Xiao, S., Tan, X., Li, Z., Wang, H.: Near space hypersonic target MCT tracking model. J. Projectiles Rockets Missiles Guidance 33(1), 185–194 (2013)Google Scholar
  5. 5.
    Cao, Y., Li, Y.: State estimation algorithm based on high speed-acceleration target in near space. Modern Defence Technol. 41(6), 97–101 (2013)Google Scholar
  6. 6.
    Guo, X., Liu, C., Zhang, Y., Wei, G., Wang, G.: Tracking algorithms for near space hypersonic target. Command Control Simul. 38(5), 8–12 (2016)Google Scholar
  7. 7.
    Wang, G., Li, J., Zhang, X., Wu, W.: A tracking model for near space hypersonic slippage leap maneuvering target. Acta Aeronautica et Astronautica Sinica 36(7), 2400–2410 (2015)Google Scholar
  8. 8.
    Liu, Y., Feng, X., Ye, Y., Wang, Y.: Improved current statistical model and adaptive tracking algorithm. Sci. Technol. Eng. 13(22), 6464–6468 (2013)Google Scholar
  9. 9.
    Shi, L., Wang, X., Xiao, S.: Adaptive data rate tracking of phased array radar based on residue norm. Shipboard Electron. Countermeasure 28(5), 45–47 (2005)Google Scholar
  10. 10.
    Xiaohua, N., Yiming, X.: Flight trajectory modeling and simulation for target tracking on NSHV. Comput. Simul. 33(3), 41–46 (2016)Google Scholar

Copyright information

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

Authors and Affiliations

  • Luyao Cui
    • 1
    Email author
  • Aijun Liu
    • 1
  • Changjun Yv
    • 1
  • Taifan Quan
    • 1
  1. 1.School of Information and Electrical EngineeringHarbin Institute of TechnologyWeihaiChina

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