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

Adaptive sensor selection for target tracking using particle filter

Adaptive sensor selection for target tracking using particle filter

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.

This study presents a novel particle filtering approach for multiple sensor target tracking. In contrast to the standard form, each particle only uses the measurements received by a single selected sensor to estimate the mean and covariance of the target state. In order to do so, sensor selections are sampled using a particle filter and the hidden states are marginalised over. Finite number of sensors allows the exact calculation of the normalisation constant, thus the sampling can be done from the optimal importance distribution. In addition, an extension to the multiple sensor case of the probabilistic data association approach is also provided when clutter or false alarm is considered. Simulation examples that involve tracking a bearings-only target are provided to demonstrate the effectiveness of the proposed algorithms in critical situations where the single-sensor observability is lacking.

References

    1. 1)
      • 1. Doucet, A., Freitas, N.D., Gordon, N.: ‘Sequential Monte Carlo methods in practice’ (Springer, New York, 2001).
    2. 2)
      • 2. Ristic, B., Arulampalam, S., Gordon, N.: ‘Beyond the Kalman filter’ (Artech House, Boston, 2004).
    3. 3)
    4. 4)
      • 4. Vemula, M., Djurić, P.M.: ‘Multisensor fusion for target tracking using sequential Monte Carlo methods’. Proc. IEEE Workshop on Statistical Signal Processing, Bordeaux, France, July 2005, pp. 13041309.
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • 8. Jiang, B., Ravindran, B.: ‘Completely distributed particle filters for target tracking in sensor networks’. Proc. IEEE Int. Parallel and Distributed Processing Symp., Anchorage, AK, USA, May 2011, pp. 334344.
    9. 9)
    10. 10)
    11. 11)
      • 11. Hovland, G.E., McCarragher, B.J.: ‘Dynamic sensor selection for robotic systems’. Proc. IEEE Int. Conf. on Robotics and Automation, Albuquerque, NM, USA, April 1997, pp. 272277.
    12. 12)
    13. 13)
      • 13. Wang, H., Yao, K., Pottie, G., Estrin, D.: ‘Entropy-based sensor selection heuristic for target localization’. Int. Symp. on Information Processing in Sensor Networks, Berkeley, CA, USA, April 2004, pp. 3645.
    14. 14)
      • 14. Zhao, F., Guibas, L.: ‘Wireless sensor networks: an information processing approach’ (Elsevier-Morgan Kaufmann, Boston, 2004).
    15. 15)
    16. 16)
      • 16. Doucet, A., Vo, B.N., Andrieu, C., Davy, M.: ‘Particle filtering for multi-target tracking and sensor management’. Proc. Int. Conf. on Information Fusion, Annapolis, MD, USA, July 2002, pp. 474481.
    17. 17)
    18. 18)
    19. 19)
      • 19. Hero III, A.O., Kreucher, C.M.: ‘Network sensor management for tracking and localization’. Proc. Int. Conf. on Information Fusion, Quebec, QC, Canada, July 2007, pp. 18.
    20. 20)
    21. 21)
      • 21. Mohammadi, A., Asif, A.: ‘Decentralized sensor selection based on the distributed posterior Cramer-Rao lower bound’. Proc. Int. Conf. on Information Fusion, Singapore, September 2012, pp. 16681675.
    22. 22)
    23. 23)
      • 23. Duan, Z., Li, X.: ‘Optimal distributed estimation fusion with transformed data’. Proc. Int. Conf. on Information Fusion, Cologne, Germany, July 2008, pp. 17.
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 27. Mustiere, F., Bolic, M., Bouchard, M.: ‘Rao-Blackwellised particle filters: examples of applications’. Proc. Canadian Conf. on Electrical Computing Engineering, Ottawa, ON, Canada, May 2006, pp. 11961200.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
      • 31. Evers, C., Hopgood, J.R.: ‘Marginalization of static observation parameters in a Rao-Blackwellized particle filter with application to sequential blind speech dereverberation’. Proc. European Signal Processing Conf., Glasgow, Scotland, UK, August 2009, pp. 14371441.
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0169
Loading

Related content

content/journals/10.1049/iet-spr.2013.0169
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address