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

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.

LoRa 2.4 GHz Communication Link and Range

“…Consequently, this technology can offer a balance for applications that require a higher data rate than LPWANs and a longer communication range than classic GHz technologies such as Wi-Fi and Bluetooth. Additionally, a higher bandwidth also allows for more accurate time-based localization [ 6 ]. Thus, LoRa at GHz represents an interesting solution for a variety of applications that involve indoor localization, such as warehouse management, but also for applications that require outdoor localization, such as construction site monitoring, livestock tracking, etc.…”

A Multimodal Localization Framework Design for IoT Applications

“…Our future work includes implementing the framework in real use cases with controlled private LPWANS to validate the feasibility of achieving lower estimation errors in such scenarios. For example, the area of interest of a smart port could be covered by a private LoRaWAN network with TDoA and AoA-enabled gateways for outdoor localization, resulting in a mean error of 122 instead of 1000 [ 14 ]. Similar to the construction equipment example in Section 4.2 , accurate indoor localization for warehouse management can be set up with a DASH7 network.…”

“…The next step verifies if the input data contain AoA measurements. If so, the estimate that was calculated with the chosen localization method can be combined with AoA as described in [14]. Finally, previous location estimates for a device in the local database can refine the new location estimate, e.g., via Bayesian filtering [19].…”

“…However, BniLam et al introduced a low-cost sub-GHz AoA gateway which could be used to implement AoA for LoRaWAN [ 13 ]. This gateway was recently used to combine TDoA and AoA localization in an outdoor LoRaWAN network that covers a dense urban area [ 14 ]. In this previous research, a mean error of 122 is obtained by using a particle filter to combine TDoA and AoA.…”

“…The estimation accuracy that can be achieved depends on the number of LoRaWAN gateways that are present on the construction site, and on the features that they support. The presence of two synchronized gateways and one AoA-enabled gateway allows for a combination of TDoA and AoA localization, which can lead to a mean error of 122 [ 14 ]. As our device contains an accelerometer, sensor data can also be taken into account to refine the location estimates.…”