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Target Tracking using Signal Strength Differences for Long-Range IoT Networks

Li, Xuhong LU ; Abou Nasa, Mohamad LU ; Rezaei, Farshid LU and Tufvesson, Fredrik LU orcid (2020) 2020 IEEE International Conference on Communications Workshops (ICC Workshops)
Abstract
Radio based positioning or tracking solutions typically require wideband signals or phase coherent antennas. In this paper, we present a target tracking method based on received non-coherent signal strength differences (RSSDs) between antennas for outdoor Internet-of-things (IoT) scenarios. We introduce an RSSD model based on classical path-loss models. With known antenna patterns and antenna array geometries, the RSSD model enables direct mapping between RSSD and angle of arrival, without involving parameters like transmit power, path-loss coefficient, etc. The RSSD model is then exploited in a recursive Bayesian filtering method for target tracking where a particle filter-based implementation is used. The performance is evaluated using... (More)
Radio based positioning or tracking solutions typically require wideband signals or phase coherent antennas. In this paper, we present a target tracking method based on received non-coherent signal strength differences (RSSDs) between antennas for outdoor Internet-of-things (IoT) scenarios. We introduce an RSSD model based on classical path-loss models. With known antenna patterns and antenna array geometries, the RSSD model enables direct mapping between RSSD and angle of arrival, without involving parameters like transmit power, path-loss coefficient, etc. The RSSD model is then exploited in a recursive Bayesian filtering method for target tracking where a particle filter-based implementation is used. The performance is evaluated using outdoor measurements in a low-power wide area network (LoRaWAN) based IoT system. Besides, we also investigate the potential of the RSSD model for AoA estimation. The experimental results show the capability of the proposed framework for real-time target/AoA tracking; reasonable accuracy is achieved even when using non-averaged RSS measurements and under non line-of-sight (NLoS) conditions. Furthermore, the non-coherent approach has low computational complexity, scales well, and is flexible to allow for different antenna array configurations.
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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
8th IEEE ICC Workshop on Advances in Network Localization and Navigation (ANLN)​​​​​​​, 2020
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 IEEE International Conference on Communications Workshops (ICC Workshops)<br/>
conference location
Dublin, Ireland
conference dates
2020-06-07 - 2020-06-11
external identifiers
  • scopus:85090269351
ISBN
978-1-7281-7441-9
978-1-7281-7440-2
DOI
10.1109/ICCWorkshops49005.2020.9145373
language
English
LU publication?
yes
id
90df32cf-db4b-4ad8-937a-c9b27b2ca0ef
date added to LUP
2020-08-05 14:51:37
date last changed
2024-05-29 17:37:30
@inproceedings{90df32cf-db4b-4ad8-937a-c9b27b2ca0ef,
  abstract     = {{Radio based positioning or tracking solutions typically require wideband signals or phase coherent antennas. In this paper, we present a target tracking method based on received non-coherent signal strength differences (RSSDs) between antennas for outdoor Internet-of-things (IoT) scenarios. We introduce an RSSD model based on classical path-loss models. With known antenna patterns and antenna array geometries, the RSSD model enables direct mapping between RSSD and angle of arrival, without involving parameters like transmit power, path-loss coefficient, etc. The RSSD model is then exploited in a recursive Bayesian filtering method for target tracking where a particle filter-based implementation is used. The performance is evaluated using outdoor measurements in a low-power wide area network (LoRaWAN) based IoT system. Besides, we also investigate the potential of the RSSD model for AoA estimation. The experimental results show the capability of the proposed framework for real-time target/AoA tracking; reasonable accuracy is achieved even when using non-averaged RSS measurements and under non line-of-sight (NLoS) conditions. Furthermore, the non-coherent approach has low computational complexity, scales well, and is flexible to allow for different antenna array configurations.<br/>}},
  author       = {{Li, Xuhong and Abou Nasa, Mohamad and Rezaei, Farshid and Tufvesson, Fredrik}},
  booktitle    = {{8th IEEE ICC Workshop on Advances in Network Localization and Navigation (ANLN)​​​​​​​, 2020}},
  isbn         = {{978-1-7281-7441-9}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Target Tracking using Signal Strength Differences for Long-Range IoT Networks}},
  url          = {{https://lup.lub.lu.se/search/files/96038631/ICC_workshop_2020_final.pdf}},
  doi          = {{10.1109/ICCWorkshops49005.2020.9145373}},
  year         = {{2020}},
}