Combining TDoA and AoA with a Particle Filter in an Outdoor LoRaWAN Network

Michiel Aernouts, Noori BniLam, Nico Podevijn, David Plets, Wout Joseph, Rafael Berkvens and Maarten Weyn

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.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 1060 - 1069
Cite this article: Aernouts, Michiel, BniLam, Noori, Podevijn, Nico, Plets, David, Joseph, Wout, Berkvens, Rafael, Weyn, Maarten, "Combining TDoA and AoA with a Particle Filter in an Outdoor LoRaWAN Network," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1060-1069.
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