access icon free Joint detection, tracking and classification of a manoeuvring target in the finite set statistics framework

© The Institution of Engineering and Technology
Received 08/09/2013, Accepted 09/05/2014, Revised 15/01/2014, Published 23/02/2015

Target detection, tracking and classification are three essential and closely coupled subjects for most surveillance systems. In the finite set statistics (FISST) framework, this paper presents a Bayesian and recursive solution to joint detection, tracking and classification (JDTC) of a manoeuvring target in a cluttered environment, which is inspired by previous work on joint target tracking and classification in the classical Bayesian filter framework. The derived JDTC algorithm exploits the dependence of target state on target class by using class-dependent dynamical model sets. The relative merits of this JDTC algorithm are demonstrated via a two-dimensional example using a sequential Monte Carlo implementation. It is shown that handling those three closely coupled subjects jointly can achieve comparable detection and tracking performance to that of the exact filter in the FISST framework with a prior known class. The classification results are consistent with the previous work.

Inspec keywords: Monte Carlo methods; Bayes methods; signal classification; filtering theory; recursive estimation; object detection; target tracking

Other keywords: recursive method; manoeuvring target tracking; FISST; joint target tracking; manoeuvring target detection; joint detection tracking and classification; classical Bayesian filter; class dependent dynamical model sets; manoeuvring target classification; surveillance systems; cluttered environment; finite set statistics; sequential Monte Carlo implementation; JDTC algorithm

Subjects: Monte Carlo methods; Filtering methods in signal processing; Monte Carlo methods; Signal processing theory; Signal detection

References

    1. 1)
      • 22. Zajic, T., Ravichandra, B., Mahler, R., et al: ‘Joint tracking and identification with robustness against unmodeled targets’. Proc. of SPIE Signal Processing, Sensor Fusion and Target Recognition XII, Orlando, FL, April 2003, pp. 279–290.
    2. 2)
    3. 3)
    4. 4)
      • 23. Vercauteren, T., Wang, X.: ‘Multitarget tracking and classification in collaborative sensor networks via sequential Monte Carlo methods’, in Haykin, S. (Ed.): ‘Handbook on array processing and sensor networks’ (John Wiley & Sons, 2009), pp. 439–467.
    5. 5)
      • 13. Mahler, R.: ‘Statistical multisource-multitarget information fusion’ (Artech House, Norwood, MA, 2007).
    6. 6)
    7. 7)
    8. 8)
      • 1. Blackman, S., Popoli, R.: ‘Design and analysis of modern tracking systems’ (Artech House, Norwood, MA, 1999).
    9. 9)
    10. 10)
    11. 11)
      • 28. Hussein, I.I., DeMars, K.J., Fruh, C., et al: ‘An AEGIS-FISST integrated detection and tracking approach to space situational awareness’. Proc. of the 15th Int. Conf. on Information Fusion, Singapore, July 2012, pp. 2065–2072.
    12. 12)
    13. 13)
      • 29. Hussein, I.I., Erwin, R.S.: ‘An AEGIS-FISST algorithm for multiple object tracking in space situational awareness’. Proc. of the AAS/AIAA Astrodynamics Specialist Conf., Minneapolis, Minnesota, September 2012, pp. 1068–1087.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 25. Gordon, N., Maskell, S., Kirubarajan, T.: ‘Efficient particle filters for joint tracking and classification’. Proc. of SPIE Signal and Data Processing of Small Targets, Orlando, FL, April 2002, pp. 439–449.
    19. 19)
    20. 20)
      • 7. Yang, W., Fu, Y.W., Li, X.: ‘Joint detection, tracking and classification of multiple maneuvering targets based on the linear Gaussian jump Markov probability hypothesis density filter’, Opt. Eng., 2013, 52, (8), pp. (083016)1–12.
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0363
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