Open AccessInvestigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks
Author(s) -
K. Vadivukkarasi,
Ritesh Kumar
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i2.pp1943-1951
Subject(s) - multilateration , computer science , wireless sensor network , kalman filter , real time computing , tracking (education) , time of arrival , received signal strength indication , tracking system , node (physics) , position (finance) , wireless , artificial intelligence , telecommunications , computer network , engineering , psychology , pedagogy , structural engineering , finance , economics
Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.