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Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE

Published on November 2012 by P. Ganeshwari, S. Diwakaran
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International Conference on Electronics, Communication and Information systems
Foundation of Computer Science USA
ICECI - Number 2
November 2012
Authors: P. Ganeshwari, S. Diwakaran
58674493-e496-47c2-95fb-bfaf14200191

P. Ganeshwari, S. Diwakaran . Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE. International Conference on Electronics, Communication and Information systems. ICECI, 2 (November 2012), 17-20.

@article{
author = { P. Ganeshwari, S. Diwakaran },
title = { Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE },
journal = { International Conference on Electronics, Communication and Information systems },
issue_date = { November 2012 },
volume = { ICECI },
number = { 2 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 17-20 },
numpages = 4,
url = { /specialissues/iceci/number2/9468-1016/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Electronics, Communication and Information systems
%A P. Ganeshwari
%A S. Diwakaran
%T Multiple Object Tracking in Wireless Sensor Network based on the Mixture of KF and MLE
%J International Conference on Electronics, Communication and Information systems
%@ 0975-8887
%V ICECI
%N 2
%P 17-20
%D 2012
%I International Journal of Computer Applications
Abstract

Wireless Sensor Networks (WSNs) has wide variety of applications and object tracking is one among that applications. In this paper, tracking of multiple objects is considered. The Extended Kalman Filter (EKF) algorithms are mostly used to get a linear form from the nonlinear states and then applying the standard Kalman filter for updating the state estimate. However, a significant drawback of the EKF algorithms is that the resulting estimation of the state diverges from its original state in many applications. In order to solve this problem one measurement conversion method Maximum Likelihood Estimation (MLE) is proposed in this paper. The analysis is based on object tracking in multiple sensor system. The object tracking is difficult because sensors have to be send data from other sensors to the head with same timestamps. This is the well known data association problem and a method which is combination of Kalman Filter (KF) and Maximum Likelihood Method is proposed to solve this problem.

References
  1. Xingbo Wang,Minyue Fu and Huanshui Zhang, "Target Tracking in Wireless sensor networks based on the combination of KF and MLE using distance measurements",IEEE Transactions on Mobile Computing,vol 11,No. 4,April 2012.
  2. J. Lin,W. Xiao,F. Lewis,and L. Xie, "Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks",IEEE Trans. Instrumentation and Measurement,pp. 1-11,oct. 2008.
  3. S. Julier and J. Uhlmann, "Unscented Filtering and Nonlinear Estimation ", proc. IEEE, vol. 92, no. 401-422, Mar. 2004.
  4. Z. L. Zhao, X. R. Li, and V. P. Jilkov,"Best Linear Unbiased Filtering with Nonlinear Measurements for Target Tracking," IEEE Trans. Aerospace Electronic Systems, vol. 40, no. 4, pp. 1324-1336,oct. 2004.
  5. X. Sheng and Y. H. Hu,"Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless sensor Networks," IEEE Trans. Signal Processing, vol. 53, no. 1,pp. 44-53,jan. 2005.
  6. T. Kleine-Ostmann and A. E. Bell,"A Data Fusion Architecture for Enhanced Position Estimation in WirelessNetworks,"IEEEcomm. Letters,vol. 5,no. 8,pp. 343-345,Aug. 2001.
  7. T. Zhao and A. Nehorai, "Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks," IEEE Trans. Signal Processing, vol. 55,no. 9, pp. 4669-4682, Sept. 2007.
  8. W. -P. Chen, J. C. Hou, and L. Sha, "Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks," IEEE Trans. Mobile Computing, vol. 3, no. 3, pp. 258-271, July 2004.
  9. M. Noel, P. Joshi, and T. Jannett, "Improved Maximum Likelihood Estimation of Target Position in Wireless Sensor Networks Using Particle Swarm Optimization," Proc. Third Int'l Conf. Information Technology: New Generations, Apr. 2006.
  10. T. Zhao and A. Nehorai, "Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks," IEEE Trans. Signal Processing, vol. 55, no. 9, pp. 4669-4682, Sept. 2007.
  11. L. M. Kaplan, "Global Node Selection for Localization in a Distributed Sensor Network," IEEE Trans. Aerospace Electronics Systems, vol. 42, no. 1, pp. 113-135, Jan. 2006.
  12. Y. Liu and Z. Sun, "EKF-Based Adaptive Sensor Scheduling for Target Tracking," Proc. IEEE Int'l Symp. Information Science and Eng. (ISISE), vol. 2, pp. 171-174, Dec. 2008.
  13. S. Banerjee and S. Khuller, "A Clustering Scheme for Hierarchical Control in Multi-Hop Wireless Networks," Proc. IEEE INFOCOM, Apr. 2001.
  14. M. G. Rabbat and R. D. Nowak, "Decentralized Source Localization and Tracking," Proc. IEEE Int'l Conf. Acoustics, Speech, and SignalProcessing, vol. 3, pp. 921-924, May 2004.
  15. M. McGuire, K. N. Plataniotis, and A. N. Venetsanopoulos, "Data Fusion of Power and Time Measurements for Mobile Terminal Location," IEEE Trans. Mobile Computing, vol. 4, no. 2, pp. 142-153, Mar. /Apr. 2005.
  16. X. R. Li and V. P. Jilkov, "A Survey of Maneuvering Target Tracking CPart IV: Decision-Based Methods,"Proc. SPIE Conf. Signal and Data Processing of Small Targets, pp. 511-534, Apr. 2002.
  17. Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, "Estimation with Application to Tracking and Navigation. " John Wiley, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks (wsns) Object Tracking Extended Kalman Filter (ekf) Kalman Filter (kf)