Research Article
A New Resampling Parameter Algorithm for Kullback-Leibler Distance with Adjusted Variance and Gradient Data Based on Particle Filter
@INPROCEEDINGS{10.1007/978-3-319-74176-5_30, author={Nga Ly-Tu and Thuong Le-Tien and Linh Mai}, title={A New Resampling Parameter Algorithm for Kullback-Leibler Distance with Adjusted Variance and Gradient Data Based on Particle Filter}, proceedings={Industrial Networks and Intelligent Systems. 3rd International Conference, INISCOM 2017, Ho Chi Minh City, Vietnam, September 4, 2017, Proceedings}, proceedings_a={INISCOM}, year={2018}, month={1}, keywords={SIR Bound error KLD-resampling RSS}, doi={10.1007/978-3-319-74176-5_30} }- Nga Ly-Tu
Thuong Le-Tien
Linh Mai
Year: 2018
A New Resampling Parameter Algorithm for Kullback-Leibler Distance with Adjusted Variance and Gradient Data Based on Particle Filter
INISCOM
Springer
DOI: 10.1007/978-3-319-74176-5_30
Abstract
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.
