Real-time self-tracking in the Internet of Things
Li Geng, Mónica F. Bugallo, Akshay Athalye, Petar M. Djuric
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.
BibTeX
@inproceedings{icassp2015_realtimeselftrac,
title = {Real-time self-tracking in the Internet of Things},
author = {Li Geng and Mónica F. Bugallo and Akshay Athalye and Petar M. Djuric},
booktitle = {ICASSP 2015},
year = {2015}
}