Recently, Semtech has released a Long Range (LoRa) chipset which operates at the globally available $2.4$ GHz frequency band, on top of the existing sub-GHz, km-range offer, enabling hardware manufacturers to design region-independent chipsets. The SX1280 LoRa module promises an ultra-long communication range while withstanding heavy interference in this widely used band. In this paper, we first provide a mathematical description of the physical layer of LoRa in the $2.4$ GHz band. Secondly, we investigate the maximum communication range of this technology in three different scenarios. Free space, indoor and urban path loss models are used to simulate the propagation of the $2.4$ GHz LoRa modulated signal at different spreading factors and bandwidths. Additionally, we investigate the corresponding data rates. The results show a maximum range of 133 km in free space, 74 m in an indoor office-like environment and 443 m in an outdoor urban context. While a maximum data rate of $253.91$ kbit/s can be achieved, the data rate at the longest possible range in every scenario equals $0.595$ kbit/s. Due to the configurable bandwidth and lower data rates, LoRa outperforms other technologies in the $2.4$ GHz band in terms of communication range. In addition, both communication and localization applications deployed in private LoRa networks can benefit from the increased bandwidth and localization accuracy of this system when compared to public sub-GHz networks.

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 expectation-maximization (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 a new algorithm to synchronize multiple individual receiving subarray antennas to form an augmented array antenna system. This augmented array antenna has a large antenna aperture that can be used for angle of arrival (AoA) estimations and it provides a better AoA estimation accuracy than the individual subarray antennas. The proposed algorithm synchronizes the time and the frequency of the different subarray antennas, using a joint maximum likelihood (ML) optimization for a data-aided received signal. The phase coherency between the subarray antennas is achieved by using a combination of the least mean square and ML algorithms. The experimental results and the simulation example show that the proposed algorithm: 1) is stable in estimating the augmented array antenna pattern; 2) provides more accurate AoA estimations than the individual subarray antennas; 3) has no limitation to the structure of the augmented array antenna in space; and 4) converges very fast. Furthermore, the experimental results prove the feasibility of using AoA estimation techniques for Internet of Things localization in outdoor environments.

The industrial demands for accurate localization systems have been rapidly increasing after the introduction of the Internet of Things (IoT) concept. Self localization and tracking transmitting sources are considered essential parts of IoT applications. In this paper we studied the possibility of applying angle of arrival (AoA) estimations to localize an IoT transceiver device in an indoor environment. Furthermore, we propose an adaptive probabilistic model which works on top of the AoA estimation technique to improve the localization accuracy. The experimental results show the potential of using AoA-based localization for indoor environments. The results furthermore show that the proposed adaptive probabilistic model outperforms the traditional static probabilistic model in terms of the localization accuracy and the stability of the position estimate.

This work introduces a novel coarse positioning system based on the estimation of the Angles-of-Arrival (AoAs) of Signals of Opportunity from a spacecraft in the Low Earth Orbit. The AoA estimation is done in real-time using phase interferometry through a dedicated hardware architecture. The 2D position is achieved by means of a simplified geometrical model based on the estimated angles. The experimental campaign aimed to assess the fit of the proposed model with reality, the AoA estimation accuracy, and the final positioning error. Results showed that the system can track the angular position of a fast-moving spacecraft with a precision of tenths of a degree. The final positioning accuracy demonstrates the effectiveness of the system for real-time coarse positioning.

Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on accurate and continuous vehicle location information. The traditional localization method based on the Global Navigation Satellite System (GNSS) is the most commonly used. However, it does not provide reliable, continuous, and accurate positioning in all scenarios, such as tunnels. Therefore, in this work, we present an algorithm that exploits the existing Vehicle-to-Infrastructure (V2I) communication channel that operates within the LTE-V frequency band to acquire in-tunnel vehicle location information. We propose a novel solution for vehicle localization based on Doppler shift and Time of Arrival measurements. Measurements performed in the Beveren tunnel in Antwerp, Belgium, are used to obtain results. A comparison between estimated positions using Extended Kalman Filter (EKF) on Doppler shift measurements and individual Kalman Filter (KF) on Doppler shift and Time of Arrival measurements is carried out to analyze the filtering methods performance. Findings show that the EKF performs better than KF, reducing the average estimation error by 10 m, while the algorithm accuracy depends on the relevant RF channel propagation conditions and other in-tunnel-related environment knowledge included in the estimation. The proposed solution can be used for monitoring the position and speed of vehicles driving in tunnel environments.