Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Channel estimation and multiple target tracking in wireless sensor networks based on quantised proximity sensors

Channel estimation and multiple target tracking in wireless sensor networks based on quantised proximity sensors

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

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

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 Wireless Sensor Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

This study presents a hybrid quantised variational/sequential Monte Carlo (SMC) method for multiple target tracking in quantised sensor networks, by considering channel estimation problem. SMC scheme is employed to attribute ambiguous observations to specific targets based on association probabilities. The associated measurements are then incorporated by the quantised variational filter (QVF), where the distribution of involved particles is approximated by a Gaussian distribution for each target. In the current work, the authors propose to jointly estimate the multiple target positions, the channel attenuation between one sensor and the cluster head, and optimise the number of quantisation bits used by the candidate sensor to quantise its measurement. The multiple target positions are estimated by using the hybrid quantised variational filtering/sequential Monte Carlo-based approach to data association. The channel attenuation is estimated by maximising the a posterior distribution and the quantisation is optimised by maximising the Fisher information. The computation of these criteria is based on the target position predictive distribution provided by the QVF algorithm. Numerical examples show that the quantisation combined with channel estimation improve the estimation performances and minimise the error estimation.

Inspec keywords: target tracking; wireless sensor networks; Monte Carlo methods; channel estimation

Other keywords: quantised proximity sensors; wireless sensor networks; Monte Carlo method; channel attenuation; quantised variational filter; multiple target tracking; channel estimation

Subjects: Communication channel equalisation and identification; Monte Carlo methods; Wireless sensor networks

References

    1. 1)
    2. 2)
    3. 3)
      • Y. Bar-Shalom , T.E. Fortmann . (1988) Tracking and data association.
    4. 4)
      • M. Beai . (2003) Variational algorithms for approximate Bayesian inference.
    5. 5)
    6. 6)
    7. 7)
      • Mansouri, M., Ouchani, I., Snoussi, H., Richard, C.: `Cramer-Rao bound-based adaptive quantization for target tracking in wireless sensor networks', IEEE/SP Workshop on Statistical Signal Processing 2009, SSP’09, 2009.
    8. 8)
    9. 9)
      • Oh, S., Russell, S., Sastry, S.: `Markov chain Monte Carlo data association for general multiple-target tracking problems', Proc. 43rd IEEE Conf. on Decision and Control, 2004, Citeseer.
    10. 10)
      • Shekarforoush, M.B.H., Zerubia, J.: `Subpixel image registration by estimating the polyphase decomposition of cross power spectrum', Proc. 1996 Conf. on Computer Vision and Pattern Recognition (CVPR 96), 1996, 359, p. 152–174.
    11. 11)
      • Liu, J., Chu, M., Reich, J.: `Resource-aware multi-target tracking in distributed sensor networks', IEEE Signal Process. Mag. Special Issue on Resour.-Constrained Signal Process. Commun. Netw., 2007, 24, p. 36–46, 3.
    12. 12)
    13. 13)
      • Snoussi, H., Richard, C.: `Ensemble learning online filtering in wireless sensor networks', IEEE ICCS Int. Conf. on Communications Systems, 2006.
    14. 14)
    15. 15)
    16. 16)
      • Teng, J., Snoussi, H., Richard, C.: `Binary variational filtering for target tracking in sensor networks', IEEE/SP 14th Workshop on Statistical Signal Processing 2007, SSP'07, 2007, p. 685–689.
    17. 17)
      • Humblot, F., Mohammad-Djafari, A.: `Super-resolution and joint segmentation in bayesian framework', 25thInt. Workshop on Bayesian Inference and Maximum Entropy Methods (MaxEnt05). AIP Conf. Proceedings, August 2005, San Jose, CA, USA, 803, p. 207–214, Available at http://ink.aip.o/ink/?APC/803/207/l.
    18. 18)
    19. 19)
      • C. Rachel , S. Keith , M. Kevin . A reactive soil moisture sensor network: design and field evaluation. Int. J. Distrib. Sensor Netw. , 2 , 149 - 162
    20. 20)
      • Barndorff-Nielsen, O.: `Exponentially decreasing distributions for the logarithm of particle size', Proc. R. Soc. Lond. A, Math. Phys. Sci., 1977, 353, p. 401–419, 1674.
    21. 21)
    22. 22)
      • Humblot, F., Collin, B., Mohammad-Djafari, A.: `Evaluation and practical issues of subpixel image registration using phase correlation methods', Int. Conf. on Physics in Signal and Image Processing (PSIP ’05), 31 January 2005, Toulouse, France, p. 115–120.
    23. 23)
      • Xue, W., Luo, Q., Chen, L., Liu, Y.: `Contour map matching lor event detection in sensor networks', Proc. 2006 ACM SIGMOD Int. Conf. on Management of Data. ACM, 2006, p. 145–156.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2010.0008
Loading

Related content

content/journals/10.1049/iet-wss.2010.0008
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address