|国家科技期刊平台
首页|期刊导航|通信与信息网络学报(英文)|Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path ModelOA

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

英文摘要

To address the issues of unstable received signal strength indicator(RSSI)and low indoor po-sitioning accuracy caused by walls and obstacles,the propagation conditions of the wireless communication system are categorized into two distinct environments:line-of-sight(LOS)and non-line-of-sight(NLOS).In the LOS environment,the traditional logarithmic path loss model is applied.For the NLOS environment,the impact of walls on signal transmission is considered,leading to the development of a multi-wall path loss model based on the T-RL method,with improvements made to the key parameter,the Fresnel coefficient R.The breakpoint value d=2.3 m in the partitioned model is determined,and the positional coordinates of the unknown nodes are calculated using the trilateration algorithm.Experimental results indicate that the T-RL based multi-wall model improves localization accuracy by 47%in NLOS envi-ronments compared to the traditional logarithmic path loss model.The average localization error using the T-RL partitioned path loss model is 0.702 1 m,representing a 55.9%improvement over the logarithmic path loss model and a 16.8%enhancement over the T-RL attenuation multi-wall model,thereby providing better environmental adaptability.

Wei Wang;Xinlin Wang;Yutong Liu;Yulin Ren;Maozhen Li;Asoke K.Nandi

School of Information and Communication Engineering,North University of China,Taiyuan 030051,ChinaSchool of Information and Communication Engineering,North University of China,Taiyuan 030051,China||Department of Electronic and Computer En-gineering,Brunel University London,Uxbridge UB8 3PH,UK

WSNindoor positioningloss modelreflec-tiontransmissionRSSI

《通信与信息网络学报(英文)》 2024 (003)

219-232 / 14

This work was supported by Shanxi Provincial Natural Science Foundation General Project under Grant 202203021221117.

评论