DocumentCode :
3433629
Title :
Real-time self-tracking in the Internet of Things
Author :
Li Geng ; Bugallo, Monica F. ; Athalye, Akshay ; Djuric, Petar M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Stony Brook Univ., Stony Brook, NY, USA
fYear :
2015
fDate :
19-24 April 2015
Firstpage :
5510
Lastpage :
5514
Abstract :
We investigate the problem of real-time self-tracking of tagged objects in a new system with low-cost “smart” tags. These tiny and battery-less devices will play a pivotal role in the infrastructure of the Internet of Things (IoT). With capabilities of low-power computation and tag-to-tag backscattered communication, no readers will be needed for running the Radio Frequency Identification (RFID) system. In order to allow for low-cost tags, self-tracking has to be performed with simple algorithms while still exhibiting high accuracy. In this paper we propose a linear observation model for which Kalman filtering (KF) is the optimal method. We also consider a nonlinear model for which we apply particle filtering (PF) of reduced complexity as the tracking method. The performance and computational complexity of the different methods are compared by computer simulations.
Keywords :
Internet of Things; Kalman filters; backscatter; object tracking; particle filtering (numerical methods); radiofrequency identification; Internet of Things; IoT; Kalman filtering; RFID system; linear observation model; low-power computation; nonlinear model; optimal method; particle filtering; radiofrequency identification; real-time object self-tracking; tag-to-tag backscattered communication; Artificial neural networks; Backscatter; Internet; Real-time systems; Sensors; Topology; Tracking; Internet of Things (IoT); Radio Frequency Identification (RFID); real-time tracking; tag-to-tag communication;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
Type :
conf
DOI :
10.1109/ICASSP.2015.7179025
Filename :
7179025
Link To Document :
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