Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2019.1639121
Monte Carlo localization algorithm based on particle swarm optimization
Cuiran Li
; School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People’s Republic of China
Jianli Xie
; School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People’s Republic of China
Wei Wu
; School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People’s Republic of China
Haoshan Tian
; School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People’s Republic of China
Yingxin Liang
; School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, People’s Republic of China
Sažetak
In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node's maximum speed of 70 m/s.
Ključne riječi
Monte Carlo localization; particle swarm optimization; adaptive step size; positioning error
Hrčak ID:
239832
URI
Datum izdavanja:
5.9.2019.
Posjeta: 619 *