Abstract
In this paper, we propose a new method to classify the bound error values of Kullback-Leibler Distance (KLD)-particle filter (PF) based Support Vector Machine (SVM) to reduce the mean number of particles used (sampling) as well as improve the performance of runtime in reality for monitoring an object. In wireless sensor network (WSN) system, the object location is calculated via the collected received signal strength (RSS) variations which are effected by furniture, walls or reflections. Therefore, we propose an architecture diagram to track an object and build the dataset model. By transforming the system state model from the 1D to 2D, the bound error value of KLD resampling can enhance estimation accuracy and convergence rate of declining number of particle used by generating a sample set near the high-likelihood region for ameliorating the effect of the RSS variations. Our proposal considers how to classify and find the bound error values of KLD PF for each iteration. The first iteration, using the observation information via KLD resampling optimal bound error to conduct a resampling on the basis of the initial bound error. From the second to the end iteration, we propose the SVM technique to search the predicted bound error value that fulfills the minimum of mean number of particle used between at the current and the next iteration. Our experiments confirm this technique to apply in reality system.
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References
Schön, T.B.: Solving nonlinear state estimation problems using particle filters-an engineering perspective. Report No. 2953, Automatic Control at Linköping University, Linköping universitet, Sweden (2010)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Sig. Process. 50(2), 174–188 (2002)
Gordon, N.J., Salmond, D.J., Smith, A.F.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In: IEE Proceedings F (Radar and Signal Processing), vol. 140, no. 2, pp. 107–113. IET Digital Library (1993)
Li, T., Sattar, T.P., Sun, S.: Deterministic resampling: unbiased sampling to avoid sample impoverishment in particle filters. Sig. Process. 92(7), 1637–1645 (2012)
Li, T., Bolic, M., Djuric, P.: Resampling methods for particle filtering: classification, implementation, and strategies. IEEE Sig. Process. Mag. 32(3), 70–86 (2015)
Li, T., Sun, S., Sattar, T.P.: Adapting sample size in particle filters through KLD-resampling. Electron. Lett. 49(12), 740–742 (2013)
Fox, D.: Adapting the sample size in particle filters through KLD-sampling. Int. J. Robot. Res. 22(12), 985–1003 (2003)
Ly-Tu, N., Le-Tien, T., Mai, L.: Performance of sampling/resampling-based particle filters applied to non-linear problems. REV J. Electron. Commun. 4(3–4), 75–83 (2016)
Park, S.-H., Kim, Y.-J., Lee, H.-C., Lim, M.-T.: Improved adaptive particle filter using adjusted variance and gradient data. In: Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Seoul, Korea, pp. 650–655, August 2008
Zhao, Q., Wei, C., Qi, L., Yuan, W.: Adaptive double-resampling particle filter algorithm for target tracking. In: International Conference on Frontier Computing, 13 July 2016, pp. 777–787. Springer, Singapore (2016)
Dihua, S., Hao, Q., Min, Z., Senlin, C., Liangyi, Y.: Adaptive KLD sampling based Monte Carlo localization. In: IEEE Proceedings Chinese Control and Decision Conference (CCDC), pp. 4154–4159 (2018)
Wang, Z., Zhao, X., Qian, X.: The analysis of localization algorithm of unscented particle filter based on RSS for linear wireless sensor networks. In: IEEE Proceedings of the 32nd Chinese Control Conference, pp. 7499–7504, 26 July 2013 (2013)
Swamynathan, M.: Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python. Apress, New York (2017)
Ly-Tu, N., Le-Tien, T., Mai, L.: A new resampling parameter algorithm for Kullback-Leibler distance with adjusted variance and gradient data based on particle filter. In: International Conference on Industrial Networks and Intelligent Systems, 4 September 2017, pp. 347–358. Springer, Cham (2017)
Ly-Tu, N., Le-Tien, T., Vo-Phu, Q., Huynh-Kha, T.: A new bound error based K-nearest neighbor for Kullback-Leibler distance particle filter in tracking. In: Proceedings National Conference, Thai Binh province, 28–29 June 2019, pp. 1–6 (2019). ISBN 978-604-67-1287-9
Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. 30(2), 169–190 (2017)
Garg, A., Upadhyaya, S., Kwiat, K.: A user behavior monitoring and profiling scheme for masquerade detection. In: Handbook of Statistics, vol. 31, pp. 353–379. Elsevier (2013)
Ly-Tu, N., Le-Tien, T., Mai, L.: A modified particle filter through Kullback-Leibler distance based on received signal strength. In: IEEE Proceedings of the 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), pp. 229–233 (2016)
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Ly-Tu, N., Vo-Phu, Q., Le-Tien, T. (2020). Using Support Vector Machine to Monitor Behavior of an Object Based WSN System. In: Le Thi, H., Le, H., Pham Dinh, T., Nguyen, N. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2019. Advances in Intelligent Systems and Computing, vol 1121. Springer, Cham. https://doi.org/10.1007/978-3-030-38364-0_16
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