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An improved resampling approach for particle filters in tracking

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conference contribution
posted on 2019-03-27, 11:05 authored by Abdullahi Daniyan, Yu GongYu Gong, Sangarapillai LambotharanSangarapillai Lambotharan
Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1, the MOD University Defence Research Collaboration (UDRC) in Signal Processing, UK and the Petroleum Technology Development Fund (PTDF), Nigeria.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

2017 22nd International Conference on Digital Signal Processing (DSP) 2017 22nd International Conference on Digital Signal Processing (DSP)

Citation

DANIYAN, A., GONG, Y. and LAMBOTHARAN, S., 2017. An improved resampling approach for particle filters in tracking. Presented at the 2017 22nd International Conference on Digital Signal Processing (DSP), London, UK, 23-25 August 2017.

Publisher

© IEEE

Version

  • AM (Accepted Manuscript)

Publication date

2017

Notes

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781538618950

ISSN

2165-3577

Language

  • en

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