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Zhou, Ning, Lau, Lawrence, Bai, Ruibin and Moore, Terry (2021) A genetic optimization resampling based particle filtering algorithm for indoor target tracking. Remote Sensing, 13 (1). p. 132. ISSN 2072-4292

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Abstract

In indoor target tracking based on wireless sensor networks, the particle filtering algorithm

has been widely used because of its outstanding performance in coping with highly non-linear

problems. Resampling is generally required to address the inherent particle degeneracy problem in

the particle filter. However, traditional resampling methods cause the problem of particle impoverishment.

This problem degrades positioning accuracy and robustness and sometimes may even

result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment

and improve positioning accuracy, this paper proposes an improved genetic optimization based

resampling method. This resampling method optimizes the distribution of resampled particles by

the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed

resampling method is then integrated into the particle filtering framework to form a genetic optimization

resampling based particle filtering (GORPF) algorithm. The performance of the GORPF

algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking

experiment. Both test results show that with the aid of the proposed resampling method, the GORPF

has better robustness against particle impoverishment and achieves better positioning accuracy than

several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable

computation load for real-time applications.

Item Type:	Article
Additional Information:	This is Gold OA
Keywords:	genetic algorithm; indoor positioning; particle filter; particle impoverishment; resampling; target tracking
Schools/Departments:	University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number:	https://doi.org/10.3390/rs13010132
Depositing User:	QIU, Lulu
Date Deposited:	04 Jun 2021 07:23
Last Modified:	04 Jun 2021 07:23
URI:	https://eprints.nottingham.ac.uk/id/eprint/65350

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