Title :
A modified probability neural network indoor positioning technique
Author :
Chih-Yung Chen ; Li-Peng Yin ; Yu-Ju Chen ; Rey-Chue Hwang
Author_Institution :
Dept. of Comput. & Commun., Shu-Te Univ., Kaohsiung, Taiwan
Abstract :
This paper presents an indoor positioning technique using a modified probabilistic neural network (MPNN) scheme. It measures the received signal strength (RSS) between an object and stations, and then transforms the RSS into distances. A MPNN engine determines coordinate of the object with the input distances. The experiments are conducted in a realistic ZigBee sensor network. The proposed approach performs significantly better than triangulation technique when the RSS data are unstable. It can be efficiently applied to applications of location based service (LBS).
Keywords :
Zigbee; indoor radio; neural nets; probability; wireless sensor networks; RSS; ZigBee sensor network; indoor positioning; location based service; modified probabilistic neural network; received signal strength; Global Positioning System; Mathematical model; Neural networks; Probabilistic logic; Vectors; Wireless communication; Wireless sensor networks; indoor positioning; modified probabilistic neural network; received signal strength; wireless sensor network;
Conference_Titel :
Information Security and Intelligence Control (ISIC), 2012 International Conference on
Conference_Location :
Yunlin
Print_ISBN :
978-1-4673-2587-5
DOI :
10.1109/ISIC.2012.6449770
