Advanced search
2 files | 7.88 MB Add to list

Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network

Author
Organization
Abstract
Internet of Things (IoT) applications that value long battery lifetime over accurate location-based services benefit from localization via Low Power Wide Area Networks (LPWANs) such as LoRaWAN. Recent work on Angle Of Arrival (AoA) estimation with LoRa enables us to explore new optimizations that decrease the estimation error and increase the reliability of Time Difference Of Arrival (TDoA) methods. In this paper, particle filtering is applied to combine TDoA and AoA measurements that were collected in a dense urban environment. The performance of this particle filter is compared to a TDoA estimator and our previous grid-based combination. The results show that a median estimation error of 199 m can be obtained with a particle filter without AoA, which is an error reduction of 10 % compared to the grid-based method. Moreover, the median error is reduced with 57 % if AoA measurements are used. Hence, more accurate and reliable localization is achieved compared to the performance of other baseline methods.

Downloads

  • WICA 955a.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 3.91 MB
  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 3.97 MB

Citation

Please use this url to cite or link to this publication:

MLA
Aernouts, Michiel, et al. “Combining TDoA and AoA with a Particle Filter in an Outdoor LoRaWAN Network.” 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE, 2020, pp. 1060–69, doi:10.1109/PLANS46316.2020.9110172.
APA
Aernouts, M., Bni Lam, N., Podevijn, N., Plets, D., Joseph, W., Berkvens, R., & Weyn, M. (2020). Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network. In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) (pp. 1060–1069). Portland, Oregon, USA: IEEE. https://doi.org/10.1109/PLANS46316.2020.9110172
Chicago author-date
Aernouts, Michiel, Noori Bni Lam, Nico Podevijn, David Plets, Wout Joseph, Rafael Berkvens, and Maarten Weyn. 2020. “Combining TDoA and AoA with a Particle Filter in an Outdoor LoRaWAN Network.” In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), 1060–69. IEEE. https://doi.org/10.1109/PLANS46316.2020.9110172.
Chicago author-date (all authors)
Aernouts, Michiel, Noori Bni Lam, Nico Podevijn, David Plets, Wout Joseph, Rafael Berkvens, and Maarten Weyn. 2020. “Combining TDoA and AoA with a Particle Filter in an Outdoor LoRaWAN Network.” In 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), 1060–1069. IEEE. doi:10.1109/PLANS46316.2020.9110172.
Vancouver
1.
Aernouts M, Bni Lam N, Podevijn N, Plets D, Joseph W, Berkvens R, et al. Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network. In: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE; 2020. p. 1060–9.
IEEE
[1]
M. Aernouts et al., “Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network,” in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, USA, 2020, pp. 1060–1069.
@inproceedings{8678465,
  abstract     = {{Internet of Things (IoT) applications that value long battery lifetime over accurate location-based services benefit from localization via Low Power Wide Area Networks (LPWANs) such as LoRaWAN. Recent work on Angle Of Arrival (AoA) estimation with LoRa enables us to explore new optimizations that decrease the estimation error and increase the reliability of Time Difference Of Arrival (TDoA) methods. In this paper, particle filtering is applied to combine TDoA and AoA measurements that were collected in a dense urban environment. The performance of this particle filter is compared to a TDoA estimator and our previous grid-based combination. The results show that a median estimation error of 199 m can be obtained with a particle filter without AoA, which is an error reduction of 10 % compared to the grid-based method. Moreover, the median error is reduced with 57 % if AoA measurements are used. Hence, more accurate and reliable localization is achieved compared to the performance of other baseline methods.}},
  author       = {{Aernouts, Michiel and Bni Lam, Noori and Podevijn, Nico and Plets, David and Joseph, Wout and Berkvens, Rafael and Weyn, Maarten}},
  booktitle    = {{2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)}},
  isbn         = {{9781728194462}},
  issn         = {{2153-358X}},
  language     = {{eng}},
  location     = {{Portland, Oregon, USA}},
  pages        = {{1060--1069}},
  publisher    = {{IEEE}},
  title        = {{Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network}},
  url          = {{http://dx.doi.org/10.1109/PLANS46316.2020.9110172}},
  year         = {{2020}},
}

Altmetric
View in Altmetric