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In this paper, we introduce a comprehensive angle of arrival (AoA) estimation solution for the long range (LoRa) communication network. Termed the LoRa array (LoRay), the proposed system constitutes hardware and software solutions to estimate the AoA of the received signals in real life urban environments. The hardware solution is based on converting multiple individual software defined radios (SDR) into a single SDR that consists of multiple RF-channels. The proposed hardware is cost effective, flexible and generic. The software solution, on the other hand, utilizes the space alternating generalized expectationmaximization (SAGE) algorithm to estimate the AoA of highly correlated received signals. The proposed software exploits few samples of the received signal to estimate the AoA of the direct and reflected paths in an intensive multipath environment. The LoRay system has been validated in outdoor urban environments. The experimental results show that the proposed system provides stable and accurate AoA estimations for both the line-of-sight (LoS) and the non-line-of-sight (NLoS) conditions. The AoA of 80% of the received signals have been estimated within an estimation error below 2 • and 10 • for the LoS and the NLoS locations, respectively.

In this paper, we present two localization algorithms that exploit the Angle of Arrival (AoA) parameters of the received signal. The proposed ANGular Location Estimation (ANGLE) algorithms utilize a probabilistic model to describe the angular response of the received signal. Consequently, the ANGLE algorithms can estimate the location of a transmitter using a single step Hadamard product. The first algorithm utilizes a Single Sample of the received signal (ANGLE-SS). The second algorithm, on the other hand, employs the signal Subspace Decomposition technique (ANGLE-SD). The localization capabilities of the ANGLE algorithms have been experimentally investigated in an office environment. The performances of the ANGLE algorithms have been validated against the performances of several AoA-based localization systems. The experimental results show that the ANGLE-SD algorithm outperforms all the studied AoA-based localization systems. The ANGLE-SS algorithm, on the other hand, outperforms every localization system that utilizes less than 50 samples of the received signal. The ANGLE algorithms are flexible, generic and computationally very efficient. These features allow the ANGLE algorithms to be easily deployed in any existing AoA-based localization system. INDEX TERMS Angle of arrival, AoA, direction of arrival, DoA, AoA-based localization systems, indoor localization systems.

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.

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