# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 1 Introduction  
Internet-of-Things (IoT) technologies have revolutionized industry operations across the globe. According to the 3 GPP standardization body, IoT deployments can be categorized into two groups: (i) terrestrial IoT, which is served by typical terrestrial cellular stations, and (ii) non-terrestrial network (NTN) IoT, which cover unmanned aerial vehicles (UAVs) and satellite networks. Due to the extensive use of IoT devices, location awareness is deemed to be beneficial in many IoT applications, with examples in asset management, tracking, logistics, environmental monitoring, and automated agriculture [1, 2]. Parallel to the growing IoT demand, the rapid advances in the space industry have increased the feasibility of IoT-over-satellite connectivity [3], thus extending the network coverage for new regions that were difficult to reach using typical terrestrial technologies. Whenever a localization solution is needed, the common strategy is to utilize the Global Navigation Satellite System (GNSS) services such as GPS, Galileo, and BeiDou, while the GNSS possesses very precise positioning capabilities, it is not always the best solution if IoT hardware constraints are a limiting factor. Such constraints are imposed by limited energy storage, computational resources, GNSS module cost, and communication overhead [1, 4]. Accordingly, alternative localization methods tailored for IoT applications are intriguing to investigate, especially if they can be utilized alongside existing communication methods without the need for a dedicated hardware module. They are particularly useful for any legacy IoT devices that are not equipped with a GNSS chip. Doppler shifts in the carrier signal have long been used in satellite-based geolocalization, where--due to the vast velocity of satellite platforms--Doppler has a distinct value dominated by the deterministic satellite movement, which is much larger than the unknown variations caused by IoT device mobility. For example, the ARGOS satellitesystem is one of the first prominent applications of Doppler measurements for locating ground transmitters [5; 6]. Other than ARGOS, Doppler geolocation has also been used in the COSPAS/SARSAT and TIROS-N [7] polar orbiting satellites, which primarily operate as search-and-rescue mission satellites [8]. In addition to Doppler measurements, angle of arrival (AoA) was one of first methods used in finding radio emitters, originally was performed by mechanically steering the receiving antenna. With the advancement in digitally controlled phase shifters and in fully-digital radio heads, multi-antenna AoA estimation can now achieve very accurate results [9; 10; 11] especially with the use of super-resolution methods such as MUSIC [12], ESPRIT [13], and weighted subspace fitting [14; 15]. AoA does not require clock synchronization with the transmitter, thus making it simpler to implement [10] than the time-of-arrival (ToA) method used in the GNSS. Rather than using AoA and Doppler measurements individually, it is compelling to investigate the effect of fusing both AoA and Doppler measurements to obtain higher accuracy. However, merging both of the measurements in the estimation problem is challenging [16]. Several algorithms are used to tackle the joint estimation problem, such as the maximum likelihood estimator (MLE) [16; 17; 18] and pseudolinear estimator, particularly the weighted instrumental variable estimator (WIVE) [19]. In this study, we investigated the performance of multi-antenna satellite-based localization by combining (i) the Doppler shift frequency signature with (ii) AoA measurements obtained at a low-Earth-orbit (LEO) satellite while it passes over an IoT device. In order to model realistic Doppler error behavior, we collected real transmissions from a LEO orbiting satellite, namely from the National Oceanic and Atmospheric Administration (NOAA) satellite, NOAA-15, using a ground station with an automated motorized tracking antenna.