Interactive Multi-model Target Maneuver Tracking Method Based on the Adaptive Probability Correction

  • Jiadong Ren
  • Xiaotong ZhangEmail author
  • Jiandang Sun
  • Qingshuang Zeng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


Non-cooperative target tracking is a key technology for complex space missions such as on-orbit service. To improve the tracking performance during the unknown maneuvering phase of the target, two methods including the IMM (interactive multi-model) algorithm based on extended CW equation and the variable IMM algorithm based on CW and extended CW equation are presented. The analysis and simulation results show that the higher the maneuvering index of the target is, the more obvious the advantages of the classical augmented IMM method are. However, the variable dimension IMM method has consistent performance for all the maneuver index interval of the target, and it is relatively suitable for engineering applications due to the lower complexity of algorithm.


Augmentation Relative navigation Target maneuver Interactive multi-model 


  1. 1.
    Liu, T., Xie, Y.: A relative navigation algorithm for a chaser tracking a non-cooperative maneuvering target in space, 31(5), 1338–1344 (2010) (In Chinese).
  2. 2.
    Qian, G.H., Li, Y., Luo, R.J.: One maneuvering frequency and the variance adaptive filtering algorithm for maneuvering target tracking. J. Radars 2(6), 258–264 (2013). (In Chinese)Google Scholar
  3. 3.
    Jiyuan, L., Jun, Z., Yingying, L.: Applying auto-adaptation filter to tracking of maneuvering target in special relative navigation. J. Northwest. Polytech. Univ. 4, 013 (2011). (In Chinese)Google Scholar
  4. 4.
    Kim, H.S., Park, J.G., Lee, D.: Adaptive fuzzy IMM algorithm for uncertain target tracking. Int. J. Control Autom. Syst. 7(6), 1001–1008 (2009)CrossRefGoogle Scholar
  5. 5.
    Liu, W., Li, Y., Wang, M.: An adaptive UPF algorithm for tracking maneuvering target in compound K noise environment. Acta Electronica Sinica 40(6), 1240–1245 (2012). (in Chinese)CrossRefGoogle Scholar
  6. 6.
    Naidu, V., Gopalaratnam, G., Shanthakumar, N.: Three Model IMM-EKF for Tracking Targets Executing Evasive Maneuvers. In: AIAA Aerospace Sciences Meeting & Exhibit (2007)Google Scholar
  7. 7.
    Yang, C., Blasch, E.: Characteristic errors of the IMM algorithm under three maneuver models for an accelerating target. In: International Conference on Information Fusion. IEEE, 2008Google Scholar
  8. 8.
    Jiangw, L.V.Z.J., Lan, Y.: IMM-CKF algorithm based on variable dimension interaction. Comput. Appl. Softw. 30(5), 4–6 (2013)Google Scholar
  9. 9.
    Sun, Q., Kong, X., Lu, C., Deng, J.: Two new IMM algorithms for nonlinear maneuvering target tracking. Electron. Opt. Control 08, 14–19+31 (2008). (in Chinese)Google Scholar
  10. 10.
    Xiong, K., Wei, C.: Spacecraft relative navigation based on multiple model adaptive estimator. J. Syst. Sci. Math. Sci. 34(07), 828–837 (2014). (in Chinese)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Shaofeng, M., Xinxi, F., Yulei, L., Zhang, W., Xiaomei, Z.: A variable dimension adaptive IMM tracking algorithm. Electron. Opt. Control 22(02), 36–40+45 (2015). (in Chinese)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jiadong Ren
    • 1
    • 2
    • 3
  • Xiaotong Zhang
    • 2
    • 3
    Email author
  • Jiandang Sun
    • 2
    • 3
  • Qingshuang Zeng
    • 1
  1. 1.School of AstronauticsHarbin Institute of TechnologyHarbinChina
  2. 2.Shanghai Institute of Spaceflight Control TechnologyShanghaiChina
  3. 3.Shanghai Key Laboratory of Space Intelligent Control TechnologyShanghaiChina

Personalised recommendations