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

fYear

2012

fDate

14-16 Aug. 2012

Firstpage

317

Lastpage

320

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Security and Intelligence Control (ISIC), 2012 International Conference on

Conference_Location

Yunlin

Print_ISBN

978-1-4673-2587-5

Type

conf

DOI

10.1109/ISIC.2012.6449770

Filename

6449770