Open AccessSTATISTICAL ANALYSIS OF WSN BASED INDOOR POSITIONING LOCALIZATION SCHEMES WITH KALMAN FILTERING
Author(s) -
A. Mohamed Rias,
A. Sivagami
Publication year - 2012
Publication title -
international journal of electronic signal and systems
Language(s) - English
Resource type - Journals
ISSN - 2231-5969
DOI - 10.47893/ijess.2012.1062
Subject(s) - wireless sensor network , computer science , kalman filter , real time computing , reliability (semiconductor) , matlab , node (physics) , indoor positioning system , positioning system , hybrid positioning system , tracking system , embedded system , computer network , artificial intelligence , engineering , power (physics) , physics , structural engineering , quantum mechanics , accelerometer , operating system
Wireless Sensor Network (WSN) is used for determining the Indoor Positioning of objects and persons since recent years. WSN has been implemented in indoor positioning applications such as real time tracking of humans/objects, patient monitoring in health care, navigation, warehouses for inventory monitoring, shopping malls, etc. But one of the problems while implementing WSN in Indoor positioning system is to ensure more coverage large number of sensors must be deployed which increases the installation cost. So in this paper, we have used MATLAB GUI named Sensor Network Localization Explorer to analyze the impact of node density on indoor positioning localization schemes. Later we have integrated the Kalman filter with the indoor positioning system to increase the reliability and reduce the localization error of the system with lesser number of nodes.