Open AccessPositioning optimisation based on particle quality prediction in wireless sensor networks
Author(s) -
Zhang Chunjiong,
Xie Tao,
Yang Kai,
Ma Hui,
Xie Yuxia,
Xu Yueyao,
Luo Pan
Publication year - 2019
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/iet-net.2018.5072
Subject(s) - particle filter , particle swarm optimization , euclidean distance , algorithm , wireless sensor network , particle (ecology) , computer science , reduction (mathematics) , filter (signal processing) , mathematics , artificial intelligence , computer vision , geometry , computer network , oceanography , geology
The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non‐linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high‐quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.