RSS-Based Localization of Low-Power IoT Devices Exploiting AoA and Range Information
Li, Xuhong LU ; Leitinger, Erik LU and Tufvesson, Fredrik LU
(2021)
55th Annual Asilomar Conference on Signals, Systems, and Computers
- Abstract
- We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The... (More)
- We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The proposed algorithm is validated using real outdoor measurements from a low-power wide area network based IoT system in a challenging scenario. Results show that the proposed algorithm can adapt to dynamic propagation conditions, and improve the localization accuracy compared to a method that exploits only single geometric feature. Furthermore, the algorithm scales well in different antenna array configurations, and is compatible with various existing IoT standards. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/497f3b3d-6afd-494b-b319-229c24dad083
- author
- Li, Xuhong
LU
; Leitinger, Erik
LU
and Tufvesson, Fredrik
LU
- organization
- publishing date
- 2021-06-03
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 54th Asilomar Conference on Signals, Systems, and Computers
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 55th Annual Asilomar Conference on Signals, Systems, and Computers
- conference location
- Pacific Groove, CA, United States
- conference dates
- 2021-10-31 - 2021-11-03
- external identifiers
-
- scopus:85107815143
- ISBN
- 978-0-7381-3126-9
- 978-1-6654-4707-2
- DOI
- 10.1109/IEEECONF51394.2020.9443542
- language
- English
- LU publication?
- yes
- id
- 497f3b3d-6afd-494b-b319-229c24dad083
- date added to LUP
- 2021-03-29 12:41:42
- date last changed
- 2024-04-18 02:58:04
@inproceedings{497f3b3d-6afd-494b-b319-229c24dad083,
abstract = {{We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The proposed algorithm is validated using real outdoor measurements from a low-power wide area network based IoT system in a challenging scenario. Results show that the proposed algorithm can adapt to dynamic propagation conditions, and improve the localization accuracy compared to a method that exploits only single geometric feature. Furthermore, the algorithm scales well in different antenna array configurations, and is compatible with various existing IoT standards.}},
author = {{Li, Xuhong and Leitinger, Erik and Tufvesson, Fredrik}},
booktitle = {{54th Asilomar Conference on Signals, Systems, and Computers}},
isbn = {{978-0-7381-3126-9}},
language = {{eng}},
month = {{06}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{RSS-Based Localization of Low-Power IoT Devices Exploiting AoA and Range Information}},
url = {{https://lup.lub.lu.se/search/files/96029194/RSS_Based_Localization_of_Low_Power_IoT_Devices_Exploiting_AoA_and_Range_Information.pdf}},
doi = {{10.1109/IEEECONF51394.2020.9443542}},
year = {{2021}},
}