Open AccessRSS‐based indoor localisation using MDCF
Author(s) -
Gui Linqing,
Yang Mengxia,
Fang Peng,
Yang Shuai
Publication year - 2017
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
ISSN - 2043-6394
DOI - 10.1049/iet-wss.2016.0085
Subject(s) - rss , computer science , fading , node (physics) , path loss , noise (video) , signal strength , particle swarm optimization , real time computing , algorithm , wireless , artificial intelligence , telecommunications , engineering , decoding methods , image (mathematics) , structural engineering , operating system
As a low‐cost distance measurement method, received signal strength (RSS) is often used for indoor wireless sensor localisation. However, RSS values can be easily influenced by multi‐path fading, noise and other environmental parameters. This decreases the accuracy and stability of estimated distance. To improve localisation accuracy, this study proposes a multiplicative distance‐correction factor (MDCF) to counteract the inaccuracy of estimated distance. In the same indoor environment, the product of this CF and estimated distance is regarded as a good approximation of real distance between unknown node and an anchor node. Then, two location estimated methods based on MDCF (MDCF‐grid and MDCF‐particle swarm optimisation) are proposed. The experimental results confirm that the proposed location estimation methods can significantly improve localisation accuracy without extra hardware in practical indoor scenarios.