Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test



To date, Wald sequential probability ratio test (WSPRT) has been widely applied to track management of multiple hypothesis tracking (MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT (SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.


多假设跟踪(MHT)的航迹管理通常采用Wald序贯概率比检测(WSPRT), 当场景中杂波密度远高于新生目标密度时, 存在真实目标航迹易被误删除的问题。同时, 由于长时间存在的目标航迹有较高的得分, 应用WSPRT将使对应航迹在目标消失很长时间之后才能被删除。为解决上述问题, 本文使用贝叶斯框架下的Shiryayev序贯概率比检测(SSPRT)替代WSPRT进行MHT的航迹管理, 通过引入假设转移概率并设置两个独立的SSPRT分别进行航迹的确认和删除。仿真结果表明, 所提出的基于SSPRT的MHT算法在高杂波密度的条件下, 能够更快地起始真实目标航迹, 同时更快地删除虚假航迹。

This is a preview of subscription content, access via your institution.


  1. 1

    Chavali P, Nehorai A. Concurrent particle filtering and data association using game theory for tracking multiple maneuvering targets. IEEE Trans Signal Proc, 2013, 61: 4934–4948

    MathSciNet  Article  Google Scholar 

  2. 2

    Taek S L, Dong L G. A probabilistic nearest neighbor filter algorithm for m validated measurements. IEEE Trans Signal Proc, 2006, 54: 2797–2802

    Article  Google Scholar 

  3. 3

    Frank A, Smyth P, Ihler A. Beyond MAP estimation with the track-oriented multiple hypothesis tracker. IEEE Trans Signal Proc, 2014, 62: 2413–2423

    MathSciNet  Article  Google Scholar 

  4. 4

    Sittler R W. An optimal data association problem in surveillance theory. IEEE Trans Mil Electron, 1964, 8: 125–139

    Article  Google Scholar 

  5. 5

    Stein J J, Blackman S S. Generalized correlation of multitarget track data. IEEE Trans Aerosp Electron Syst, 1975, 11: 1207–1217

    Article  Google Scholar 

  6. 6

    Niu R, Varshney P K. Sampling schemes for sequential detection with dependent observations. IEEE Trans Signal Proc, 2010, 58: 1469–1481

    MathSciNet  Article  Google Scholar 

  7. 7

    Suratman F Y, Zoubir A M. Bootstrap based sequential probability ratio tests. In: Proceedings of IEEE International Conference on Acoustics, Speech, Signal Process (ICASSP’13), Vancouver, 2013. 6352–6356

    Google Scholar 

  8. 8

    Demos G C, Ribas R A, Broida T J, et al. Applications of MHT to dim moving targets. In: Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Los Angeles, 1990. 297–309

    Google Scholar 

  9. 9

    Reid D B. An algorithm for tracking multiple targets. IEEE Trans Automat Contr, 1979, 24: 843–854

    Article  Google Scholar 

  10. 10

    Blackman S S. Multiple hypothesis tracking for multiple target tracking. IEEE Aerosp Electron Syst, 2004, 19: 5–18

    Article  Google Scholar 

  11. 11

    Bar-Shalom Y, Blackman S S, Fitzgerald R J. Dimensionless score function for multiple hypothesis tracking. IEEE Trans Aerosp Electron Syst, 2007, 43: 392–400

    Article  Google Scholar 

  12. 12

    Ren X Y, Luo T J, Huang Z P, et al. An efficient MHT implementation using GRASP. IEEE Trans Aerosp Electron Syst, 2014, 50: 86–101

    Article  Google Scholar 

  13. 13

    Blackman S S, Popoli R. Design and Analysis of Modern Tracking Systems. London: Artech House, 1999

    Google Scholar 

  14. 14

    Shiryayev A N. Optimal Stopping Rules. New York: Springer-Verlag, 1977

    Google Scholar 

  15. 15

    Malladi D P, Speyer J L. A generalized Shiryaev sequential probability ratio test for change detection and isolation. IEEE Trans Automat Contr, 1999, 44: 1522–1534

    MathSciNet  Article  MATH  Google Scholar 

  16. 16

    Ru J, Jilkov V P, Li X R, et al. Detection of target maneuver onset. IEEE Trans Aerosp Electron Syst, 2009, 45: 536–554

    Article  Google Scholar 

  17. 17

    Yu L, Li X R. Sequential multiple-model detection of target maneuver termination. In: Proceedings of the 14th International Conference on Information Fusion (FUSION), Chicago, 2011. 1–8

    Google Scholar 

  18. 18

    Blanding W R, Willett P K, Bar-Shalom Y, et al. Multisensor track management for targets with fluctuating SNR. IEEE Trans Aerosp Electron Syst, 2009, 45: 1275–1292

    Article  Google Scholar 

  19. 19

    Wang H, Sun J P, Lu S T, et al. Factor graph aided multiple hypothesis tracking. Sci China Inf Sci, 2013, 56: 109301

    MathSciNet  Google Scholar 

  20. 20

    Schuhmacher D, Vo B T, Vo B N. A consistent metric for performance evaluation of multiobject filters. IEEE Trans Signal Proc, 2008, 56: 3447–3457

    MathSciNet  Article  Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Jinping Sun or Songtao Lu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fu, J., Sun, J., Lu, S. et al. Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test. Sci. China Inf. Sci. 59, 122306 (2016).

Download citation


  • multiple target tracking
  • multiple hypothesis tracking
  • Shiryayev sequential probability ratio test
  • track management
  • track score


  • 多目标跟踪
  • 多假设跟踪
  • Shiryayev序贯概率比检测
  • 航迹管理
  • 航迹得分