A genetic optimization resampling based particle filtering algorithm for indoor target tracking
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/65350/ |
| _version_ | 1848800214369959936 |
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| author | Zhou, Ning Lau, Lawrence Bai, Ruibin Moore, Terry |
| author_facet | Zhou, Ning Lau, Lawrence Bai, Ruibin Moore, Terry |
| author_sort | Zhou, Ning |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:48:00Z |
| format | Article |
| id | nottingham-65350 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:48:00Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-653502021-06-04T07:23:57Z https://eprints.nottingham.ac.uk/65350/ A genetic optimization resampling based particle filtering algorithm for indoor target tracking Zhou, Ning Lau, Lawrence Bai, Ruibin Moore, Terry 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. 2021-01-02 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/65350/1/gold%203.pdf 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 genetic algorithm; indoor positioning; particle filter; particle impoverishment; resampling; target tracking http://dx.doi.org/10.3390/rs13010132 doi:10.3390/rs13010132 doi:10.3390/rs13010132 |
| spellingShingle | genetic algorithm; indoor positioning; particle filter; particle impoverishment; resampling; target tracking Zhou, Ning Lau, Lawrence Bai, Ruibin Moore, Terry A genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| title | A genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| title_full | A genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| title_fullStr | A genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| title_full_unstemmed | A genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| title_short | A genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| title_sort | genetic optimization resampling based particle filtering algorithm for indoor target tracking |
| topic | genetic algorithm; indoor positioning; particle filter; particle impoverishment; resampling; target tracking |
| url | https://eprints.nottingham.ac.uk/65350/ https://eprints.nottingham.ac.uk/65350/ https://eprints.nottingham.ac.uk/65350/ |