Open AccessIndoor 3D visible light positioning system based on adaptive parameter particle swarm optimisation
Author(s) -
Xu Shiwu,
Wu Yi,
Wang Xufang,
Wei Fen
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.1141
Subject(s) - particle swarm optimization , non line of sight propagation , algorithm , computer science , swarm behaviour , fitness function , visible light communication , simulated annealing , multipath propagation , mathematical optimization , mathematics , genetic algorithm , artificial intelligence , physics , telecommunications , optics , light emitting diode , channel (broadcasting) , wireless
Visible light positioning has become a new research hotspot in recent years. In this study, a new Gaussian model of hybrid noise and multipath reflection is established for noise interference in line‐of‐sight (LOS) communication and multipath reflection in non‐LOS (NLOS) communication. First, the likelihood function of the ranging error is established according to the Gaussian hybrid model, and the analytical expression of Cramer–Rao lower bound is derived. Second, the novel adaptive parameter particle swarm optimisation (AP‐PSO) algorithm is proposed to calculate the three‐dimensional (3D) coordinates of the target. In order to avoid falling into the local optimal solution, a novel particle adaptive mutation algorithm, namely AP‐PSO‐M algorithm is also proposed. Simulation results show that if the NLOS propagation probability is 0.3 and the swarm size is 20, compared with Newton–Raphson (NR), particle swarm optimisation (PSO), dissipative PSO (DPSO) and simulated annealing PSO (SA‐PSO) algorithms, the average positioning error based on AP‐PSO‐M algorithm can be reduced by 45.43, 38.98, 25.37 and 26.42%, respectively. If similar positioning accuracy is obtained, compared with PSO, DPSO and SA‐PSO algorithms, the average calculation time required by the AP‐PSO‐M algorithm can be reduced by 48.16, 30.24 and 26.14%, respectively.