A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

Zhou, Ning; Lau, Lawrence; Bai, Ruibin; Moore, Terry

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.

Citation

Zhou, N., Lau, L., Bai, R., & Moore, T. (2021). A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking. Remote Sensing, 13(1), Article 132. https://doi.org/10.3390/rs13010132

• Journal Article Type: Article
• Acceptance Date: Dec 30, 2020
• Online Publication Date: Jan 2, 2021
• Publication Date: Jan 2, 2021
• Deposit Date: Jan 6, 2021
• Publicly Available Date: Jan 6, 2021
• Journal: Remote Sensing
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• Article Number: 132
• DOI: https://doi.org/10.3390/rs13010132
• Keywords: General Earth and Planetary Sciences
• Public URL: https://nottingham-repository.worktribe.com/output/5202177
• Publisher URL: https://www.mdpi.com/2072-4292/13/1/132