Particle Filter based Target Tracking in Wireless Sensor Networks using Support Vector Machine

Document Type : Computer Networking-Amin Hosseini


Payame Noor University


Target tracking is estimating the state of moving targets using noisy measurements obtained at a single observation point or node. Particle filters or sequential Monte Carlo methods use a set of weighted state samples, called particles, to approximate the posterior probability distribution in a Bayesian setup. During the past few years, Particle Filters have become very popular because of their ability to process observations represented by nonlinear state-space models where the noise of the model can be non-Gaussian. There are many Particle Filter methods, and almost all of them are based on three operations: particle propagation, weight computation, and resampling. One of the main limitations of the previously proposed schemes is that their implementation in a wireless sensor network demands prohibitive communication capability since they assume that all the sensor observations are available to every processing node in the weight update step. In this paper, we use a machine learning technique called support vector machine to overcome this drawback and improve the energy consumption of sensors. Support Vector Machine (SVM) is a classifier which attempts to find a hyperplane that divides two classes with the largest margin. Given labeled training data, SVM outputs an optimal hyperplane which categorizes new examples. The training examples that are closest to the hyperplane are called support vectors. Using our approach, we could compress sensor observations and only support vectors will be communicated between neighbor sensors which lead to cost reduction in communication. We use LIBSVM library in our work and use MATLAB software to plot the results and compare the proposed protocol with CPF and DPF algorithms. Simulation results show significant reduction in the amount of data transmission over the network.


[1] E. Cayirci, H. Tezcan, Y. Dogan, and V. Coskun, “Wireless sensor networks for underwater survelliance systems”, Ad Hoc Networks, vol. 4, pp. 431-446, 2006.
[2] S. Santini, B. Ostermaier, and A. Vitaletti, “First experiences using wireless sensor networks for noise pollution monitoring”, presented at the Proceedings of the workshop on Real-world wireless sensor networks, Glasgow, Scotland, 2008.
[3] H.-W. Tsai, C.-P. Chu, and T.-S. Chen, “Mobile object tracking in wireless sensor networks”, Computer Communications, vol. 30, pp. 1811-1825, 6/8/ 2007.
[4] A. Ribeiro, G. B. Giannakis, and S. I. Roumeliotis, “SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations”, IEEE Transactions on Signal Processing, vol. 54, pp. 4782-4795, 2006.
[5] A. Dhital, P. Closas, and C. Fernandez-Prades, “Bayesian filtering for indoor localization and tracking in wireless sensor networks”, EURASIP Journal on Wireless Communications and Networking, vol. 2012, pp. 1-13, 2012.
[6] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking”, IEEE Transactions on Signal Processing, vol. 50, pp. 174-188, 2002.
[7] P. M. Djuric, M. Vemula, and M. F. Bugallo, “Target Tracking by Particle Filtering in Binary Sensor Networks”, IEEE Transactions on Signal Processing, vol. 56, pp. 2229-2238, 2008.
[8] Y. Huang, W. Liang, H.-b. Yu, and Y. Xiao, “Target tracking based on a distributed particle filter in underwater sensor networks”, Wireless Communications and Mobile Computing, vol. 8, pp. 1023-1033, 2008.
[9] S. Sarkka, “Bayesian Filtering and Smoothing”, Cambridge University Press, 2013.
[10] H. Q. Liu, H. C. So, F. K. W. Chan, and K. W. K. Lui, “Distributed particle filter for target tracking in sensor networks”, Progress In Electromagnetics Research C, vol. 11, pp. 171-182, 2009.
[11] K. Achutegui, L. Martino, J. Rodas, C. J. Escudero, and J. Miguez, “A multi-model particle filtering algorithm for indoor tracking of mobile terminals using RSS data”, in Control Applications, (CCA) & Intelligent Control, (ISIC), 2009 IEEE, 2009, pp. 1702-1707.
[12] O. Hlinka, F. Hlawatsch, and P. M. Djuric, “Distributed particle filtering in agent networks: A survey, classification, and comparison”, IEEE Signal Processing Magazine, vol. 30, pp. 61-81, 2013.
[13] A. Oracevic and S. Ozdemir, “A survey of secure target tracking algorithms for wireless sensor networks”, in Proceedings of the World Congress on Computer Applications and Information Systems (WCCAIS ’14), pp. 1–6, IEEE, Hammamet, Tunisia, January 2014.
[14] O. Demigha, W.-K. Hidouci, and T. Ahmed, “On energy efficiency in collaborative target tracking in wireless sensor network: a review”, IEEE Communications Surveys and Tutorials, vol. 15, no. 3, pp. 1210–1222, 2013.
[15] A. Ez-Zaidi, S. Rakrak, “A Comparative Study of Target Tracking Approaches in Wireless Sensor Networks”, Journal of Sensors, vol. 2016, p. 11, 2016.
[16] M. W. Khan, N. Salman, A. Ali, A. M. Khan, and A. H. Kemp, “A comparative study of target tracking with Kalman filter, extended Kalman filter and particle filter using received signal strength measurements”, in Emerging Technologies (ICET), 2015 International Conference on. IEEE, 2015.
[17] B. Jiang, B. Ravindran, “Completely Distributed Particle Filters for Target Tracking in Sensor Networks”, IEEE International Parallel & Distributed Processing Symposium (IPDPS), pp. 334-344, 2011.
[18] O. Hlinka, O. Sluciak, F. Hlawatsch, P. Djuric, M. Rupp, “Distributed Gaussian particle filtering using likelihood consensus”, in: International Conference on Acoustics, Speech and Signal Processing, pp. 3756–3759, May 2011.
[19] O. Hlinka, F. Hlawatsch, P. Djuric, “Distributed particle filtering in agent networks”, IEEE Signal Process. Mag. pp. 61-81 (January) (2013).
[20] Claudio J. Bordin, Marcelo G. S. Bruno, “Cooperative bling equalization of frequency-selective channels in sensor networks using decentralized particle filtering”, in: 42nd Asilomar Conference on Signals, Systems and Computers, pp. 1198–1201, October 2008.
[21] Mark Coates, “Distributed particle filters for sensor networks”, in: The International Conference on Information Processing in Sensor Networks, (IPSN), pp. 99–107, April 2004.
[22] O. Hlinka, P. Djuric, F. Hlawatsch, "Consensus-based distributed Particle Filtering with distributed proposal adaptation", IEEE Trans. Signal Process., vol 62, pp. 3029–3041, 2014.
[23] T. Ghirmai, "Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications", Sensors, 16, 1454, 2016.
[24] J. Read, K. Achutegui, and J. Miguez, “A distributed particle filter for nonlinear tracking in wireless sensor networks”, Signal Processing, vol. 98, pp. 121-134, 2014.
[25] A. Doucet, N. d. Freitas, and N. Gordon, “Sequential Monte Carlo Methods in Practice” Springer, 2001.
[26] B. Ristic, S. Arulampalam, and N. Gordon, “Beyond the Kalman Filter: Particle Filters for Tracking Applications”, Artech House, 2004.
[27] R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems”, Journal of Basic Engineering, vol. 82, pp. 35-45, 1960.
[28] A. Bain and D. Crisan, “Fundamentals of Stochastic Filtering”, Springer, 2009.
[29] O. Cappe, S. J. Godsill, and E. Moulines, “An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo”, Proceedings of the IEEE, vol. 95, pp. 899-924, 2007.
[30] Djuric, x, P. M., J. H. Kotecha, Z. Jianqui, H. Yufei, et al., “Particle filtering”, IEEE Signal Processing Magazine, vol. 20, pp. 19-38, 2003.
[31] L. Tiancheng, M. Bolic, and P. M. Djuric, “Resampling Methods for Particle Filtering: Classification, implementation, and strategies”, IEEE Signal Processing Magazine, vol. 32, pp. 70-86, 2015.
[32] M. Bolic, P. M. Djuric, and H. Sangjin, “Resampling algorithms and architectures for distributed particle filters”, IEEE Transactions on Signal Processing, vol. 53, pp. 2442-2450, 2005.
[33] J. Miguez, “Analysis of parallelizable resampling algorithms for particle filtering”, Signal Processing, vol. 87, pp. 3155-3174, 2007.
[34] C. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”, Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998/06/01 1998.
[36] C.-c. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines”, ACM Transactions on Intelligent Systems and Technology, vol. 2, 2011.
[37] C.-w. Hsu, C.-c. Chang, and C.-j. Lin, “A practical guide to support vector classification”, 2010.