http://iet.metastore.ingenta.com
1887

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

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

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

References

    1. 1)
      • 1. Blackman, S., Popoli, R.: ‘Design and analysis of modern tracking systems’ (Artech House, Norwood, MA, 1999).
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 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)112.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 13. Mahler, R.: ‘Statistical multisource-multitarget information fusion’ (Artech House, Norwood, MA, 2007).
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 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. 279290.
    23. 23)
      • 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. 439467.
    24. 24)
    25. 25)
      • 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. 439449.
    26. 26)
    27. 27)
    28. 28)
      • 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. 20652072.
    29. 29)
      • 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. 10681087.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0363
Loading

Related content

content/journals/10.1049/iet-spr.2013.0363
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address