Multiple Targets Classification and Fuzzy Logic Decision Fusion in Wireless Sensor Networks

Authors: Ahmad Aljaafreh

Abstract:

This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.

Keywords: Classification, decision fusion, fuzzy logic, hidden Markov model

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1090846

References:

[1] M. Winkler, K.-D. Tuchs, K. Hughes, and G. Barclay, "Theoretical and practical aspects of military wireless sensor networks,” Journal of Telecommunications and Information Technology, pp. 37–45, 2008.
[2] M. N. Raghavendra, "Collaborative classification applications in sensor networks,” 2002.
[3] T.-Y. Sun, C.-C. Liu, S.-J. Tsai, and S.-T. Hsieh, "Blind source separation with dynamic source number using adaptive neural algorithm,” Expert Syst. Appl., vol. 36, no. 5, pp. 8855–8861, 2009.
[4] E. Drakopoulos, J. J. Chao, , and C. C. Lee, "A two-level distributed multiple hypothesis decision system,” vol. 37, no. 3, pp. 380–384, Mar. 1992.
[5] J. H. Kotecha, V. Ramachandranand, and A. M. Sayeed, "Distributed multitarget classification in wireless sensor networks,” vol. 23, no. 4, pp. 703–824, Apr. 2005.
[6] D. Hall and J. Llinas, "An introduction to multisensor data fusion,” Proceedings of the IEEE, vol. 85, no. 1, pp. 6–23, Jan 1997.
[7] R. Viswanathan and P. Varshney, "Distributed detection with multiple sensors i. fundamentals,” Proceedings of the IEEE, vol. 85, no. 1, pp. 54–63, Jan 1997.
[8] S. Jayaweera, "Bayesian fusion performance and system optimization for distributed stochastic gaussian signal detection under communication constraints,” Signal Processing, IEEE Transactions on, vol. 55, no. 4, pp. 1238–1250, April 2007.
[9] Q. Tian and E. Coyle, "Optimal distributed detection in clustered wireless sensor networks,” Signal Processing, IEEE Transactions on, vol. 55, no. 7, pp. 3892–3904, July 2007.
[10] S. Jayaweera, "Decentralized detection of stochastic signals in powerconstrained sensor networks,” in Signal Processing Advances in Wireless Communications, 2005 IEEE 6th Workshop on, June 2005, pp. 270–274.
[11] B. Malhotra, I. Nikolaidis, and J. Harms, "Distributed classification of acoustic targets in wireless audio-sensor networks,” Comput. Netw., vol. 52, no. 13, pp. 2582–2593, 2008.
[12] ”http://kidsvid.altec.org’, "Atmospheric sound absorption calculator,” 2009.
[13] W. J. Roberts, H. W. Sabrin, and Y. Ephraim, "Ground vehicle classification using hidden markov models,” in Atlantic coast technologies Inc., Silver Spring MD, 2001.
[14] Y. Kim, S. Jeong, D. Kim, and T. S. L´opez, "An efficient scheme of target classification and information fusion in wireless sensor networks,” Personal Ubiquitous Comput., vol. 13, no. 7, pp. 499–508, 2009.
[15] L. Lam and S. Suen, "Application of majority voting to pattern recognition: an analysis of its behavior and performance,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 27, no. 5, pp. 553–568, Sep 1997.