A New Resampling Parameter Algorithm for Kullback-Leibler Distance with Adjusted Variance and Gradient Data Based on Particle Filter
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In this paper, we propose a new resampling method of particle filter (PF) to monitor target position. The target location is to improve enhancing the effect of the received signal strength (RSS) variations. The key issue of our technique is to determine a new resampling parameter that finding the optimal bound error and lower bound variance values for Kullback-Leibler distance (KLD)-resampling adjusted variance and gradient data based on PF to ameliorate the effect of the RSS variations by generating a sample set near the high-likelihood region. To find these values, these optimal algorithms are proposed based on the maximum mean number of particles used of our proposal and other KLD-resampling methods. Our experiments show that the new technique does not only enhance the estimation accuracy but also improves the efficient number of particles compared to the traditional methods.
KeywordsSIR Bound error KLD-resampling RSS
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number T2016-02-IT.
- 2.Schön, T.B.: Solving Nonlinear State Estimation Problems Using Particle Filters-An Engineering Perspective. Department of Automatic Control, Linköping University, Linköping (2010)Google Scholar
- 6.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. IET (1993)Google Scholar
- 9.Ly-Tu, N., et al.: Performance of sampling/resampling-based particle filters applied to non-linear problems. REV J. Electron. Commun. 4(3–4) (2014)Google Scholar
- 10.Park, S.-H., et al.: Improved adaptive particle filter using adjusted variance and gradient data. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2008. IEEE (2008)Google Scholar
- 12.Ly-Tu, N., Le-Tien, T., Mai, L.: A modified particle filter through Kullback-Leibler distance based on received signal strength. In: 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS). IEEE (2016)Google Scholar
- 13.Wang, Z., Zhao, X., Qian, X.: Unscented particle filter with systematic resampling localization algorithm based on RSS for mobile wireless sensor networks. In: 2012 Eighth International Conference on Mobile Ad-hoc and Sensor Networks (MSN). IEEE (2012)Google Scholar
- 14.Ly-Tu, N., Mai, L., Le-Tien, T.: KLD-resampling with adjusted variance and gradient data-based particle filter applied to wireless sensor networks. In: 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS). IEEE (2015)Google Scholar
- 15.Ly-Tu, N., Le-Tien, T., Mai, L.: A study on particle filter based on KLD-resampling for wireless patient tracking. Int. J. Industr. Eng. Manage. Syst. 92–102 (2017). ISSN 1598-7248 (Print). ISSN 2234-6473 (Online). Publisher: The Korean Institute of Industrial EngineersGoogle Scholar
- 16.Wang, Z., Zhao, X., Qian, X.: The analysis of localization algorithm of unscented particle filter based on RSS for linear wireless sensor networks. In: 2013 32nd Chinese Control Conference (CCC). IEEE (2013)Google Scholar
- 17.Ly-Tu, N., Le-Tien, T., Vo-Thi-Luu, P., Mai, L.: Particle filter through Kullback-Leibler distance resampling with adjusted variance and gradient data for wireless biomedical sensor networks. In: Proceedings on International Conference on Ubiquitous Information Management and Communication (IMCOM 2015), Bali, Indonesia, 8–10 January 2015. ISBN 978-1-4503-3377-1. http://dx.doi.org/10.1145/2701126.2701221