Leveraging carrier phase observations within Global Navigation Satellite Systems receivers allows centimeter-level positioning accuracy. However, carrier phase observations are significantly affected by additive noise, which is assumed to follow a von Mises distribution, thereby degrading the performance of phase-based positioning estimators. To improve the modeling of carrier phase observations, we propose a novel approach that constrains the parameters of the von Mises distribution-specifically, the angular location modeling the phase and its dispersion parameter kappa modeling the noise-to evolve within the Lie group space SO(2) x R+. To estimate these parameters, we employ a Lie group maximum likelihood estimator, solved through a Newton algorithm on Lie groups. This approach demonstrates advantages in terms of robustness and precision, especially when dealing with a small number of observations, compared to traditional Euclidean-based methods.
Low Earth Orbit (LEO) mega-constellations are revolutionizing global communication by delivering high-speed internet and supporting a wide range of services. Beyond communication, recent studies have shown that LEO satellites can also provide Positioning, Navigation, and Timing (PNT) services. Leveraging existing LEO Non-terrestrial-network (NTN) for dual-purpose applications has garnered significant attention as a future-oriented approach. This paper investigates the feasibility of re-purposing a well-established satellite communication protocol, Digital Video Broadcasting Second Generation Satellite Extensions (DVB-S2X), for positioning applications within LEONTN. This paper analyses the statistical acquisition performance similar to the Global Navigation Satellite System (GNSS) to evaluate the potential of DVB-S2X for positioning estimation. This analysis can provide a baseline for trade-offs for communication protocol selection when developing a sustainable integrated navigation and communication (Nav-Comm) system within LEONTN.
Global Navigation Satellite Systems (GNSS) are the primary technology for delivering reliable positioning and timing solutions, supporting a wide range of applications across different sectors. However, their performance can be significantly degraded by interference, which gives rise to the need for robust countermeasures that ensure both accuracy and resilience. This paper presents a testbed based on the Skydel GNSS Software Simulator for conducting multi-antenna GNSS experiments under realworld conditions, enabling interference mitigation studies without the need for additional hardware. The output is adjusted and sent to the FGI-GSRx GNSS software receiver, which evaluates the impact of the beamforming algorithms across all receiver stages, providing a comprehensive assessment of their effect on positioning performance. Two well-known techniques, Power Inversion (PI) and Capon (CAP), are analyzed within the context of small receivers, with results demonstrating their potential for interference mitigation even with limited antenna arrays, thus proving their suitability for handheld devices.
Indoor Positioning has grown considerably in recent years due to technological advancements and its usefulness for product tracking, pedestrian navigation, or autonomous driving. Indoor Positioning systems comprise three main components: the supporting technology, the underlying measurements, and the position estimation algorithm/method. One measurement that has been attracting attention recently is Direction of Arrival (DoA). DoA represents the direction from which a signal is impinging on a receiver. However, DoA measurements are affected by noise, interference, multipath propagation, and obstructions in the Line of Sight (LoS). In this work, we propose an approach to mitigate these problems and improve DoA estimation by applying filtering to the phase-differences obtained from each pair of elements in Linear Antenna Arrays. The results show that the accuracy in DoA estimation using filtering benefits the most inside the Field-of-View (FoV) of -40 degrees to 40 degrees. Among the several discussed methods, Z-Score provides the best average error of 5.79 degrees inside the -40 degrees to 40 degrees.
In the positioning of the Global Navigation Satellite System (GNSS) in the urban environment, the insufficient number of direct paths from available satellites due to problems such as shelter of buildings, leading to the reduction of the accuracy of the positioning of traditional positioning methods. In typical urban scenarios, specular multipaths occupy the majority, which carry abundant environmental information. Therefore, a deep neural network based GNSS multipath-assisted positioning algorithm (DL-MPAL) is proposed in this paper. In this method, multipath signal features are transformed into learnable localization features to assist localization, to improve localization accuracy and robustness. Moreover, the algorithm innovatively adopts differential pseudorange and normalized satellite position as network inputs, and receiver position in East-North-Up (ENU) coordinate system as output, realizing end-to-end mapping from signal features to receiver position. The simulation results show that the DL-MPAL algorithm reaches an average positioning error of 0.87 m and 5.21 m in two different urbanization scenarios. Also, it has high robustness under different carrier-tonoise ratio and training set label error.
