The provision of communication and navigation services to lunar orbiting and surface users, for different mission phases (i.e., lunar transfer orbit, descent and landing, proximity operations), is of particular importance considering the growing number of lunar missions planned by private companies and national agencies in the next 20 years.

During the deployment of a lunar navigation satellite system, both its monitoring and its validation will require an innovative approach different from common GNSS terrestrial application, considering the absence of a ground station on the lunar surface. In this paper, a trade-off will be performed between three proposed strategies for the monitoring of navigation signals broadcasted by lunar-orbiting satellites:

• Monitoring from Earth: the direct visibility from a Ground Segment on the Earth will be analyzed in the presentation and a preliminary link budget is computed in order to derive power levels and discuss the feasibility of this strategy.
• Cross-monitoring: the analyzed constellation geometry permits extensive visibility time windows between its satellites. Since FSL (Free-Space Loss) of these links could be more representative of an orbit and/or surface user of the navigation service, link budget is computed but transmission and reception in the same frequency band shall be dealt.
• Monitoring from lunar orbit: both the predominant FSL of a Moon-to-Earth link and the issues related to reception of the navigation signal by the lunar satellites could be overcome by lunar-orbiting assets either equipping a receiver (in-orbit monitoring but without interference) or acting as data relay towards Earth (dedicated communication link with Earth-pointed antennas instead of side lobes exploitation). Link budget and visibility will be analyzed considering different orbits.

Advantages and disadvantages will be presented and discussed in order to propose the best strategy to verify structure and/or content of the navigation signal.

Last decade has seen a surge in the development and deployment of low Earth orbit (LEO) constellations primarily serving broadband communications applications. These developments have influenced the interest in providing positioning, navigation, and timing (PNT) services from LEO, thus aiming to augment the well-established global navigation satellite systems (GNSSs) in medium Earth orbit (MEO) by new signals and services. Yet another proposal is to utilize LEO constellations to monitor and detect faults in GNSSs. This approach promises an advantage over existing ground-based monitoring, primarily due to minimized atmospheric impact in the observations. In this paper we investigate the influence of LEO constellation design on the line-of-sight (LOS) visibility conditions for GNSS monitoring. We simulate a series of Walker constellations in LEO with varying number of total satellites, orbital planes, and orbital heights. From the simulated data we gather statistics on number of visible satellites, durations of visibilities, and the quality of this visibility quantified by the dilution of precision (DOP) metric. Our findings indicate a relatively small number of satellites sufficing in providing an adequate monitoring capability. We also identify orbital geometric constraints resulting in suboptimal performance and provide optimization strategies.

GNSS networks have become increasingly important over the past 15 years. Not only for the provision of DGNSS or RTK data, but also for e.g., orbit determination or interference monitoring. Current GNSS networks are based on a multitude of expensive ground stations (antenna, receiver, server, …) as well as fast data networks and central computer resources. The disadvantage of such networks is that the adaptations of these networks for new signals are usually associated with high costs and effort.

This paper proposes a new approach to development a sustainable, cost-effective, and flexible GNSS monitoring network within a defined area. The hardware for these stations should be reduced to a minimum. To reach this goal the GNSS network will build up with so-called Remote Radio Head sensors or in particular Software-Defined-Radios. These devices are connected with a short cable to the antenna and convert the GNSS signals from each station to a digital signal (IQ File). Due to the digitalization directly after the antenna, the transmission loss of the signal is reduced to a minimum. A Multi-Receiver-Vector-Tracking (MRVT) algorithm realizes the collaborative processing of all signals from all stations in this network. Within this advanced Tracking technique, a single Kalman Filter process the raw GNSS signals. The output of this algorithm are all PVT solutions from all stations. The main advantages of a MRVT algorithm are the improvement of reliability and robustness of the PVT solution. This innovative approach of GNSS networks open a variety of new applications and possibilities in the future.

The software was developed with a simulated IQ file from a signal simulator. First results from the software development will be presented and discussed within the paper.

This paper presents a thorough investigation into the EKF-based SoOP navigation algorithm's sensitivity to spatial parameters and receiver- and transmitter-related properties. Utilizing the innovative SoOPNE simulation platform, our study unveils significant insights. For instance, at high latitudes, Iridium-Next and Oneweb show a tenfold accuracy improvement over Orbcomm. Additionally, discrepancies between predicted and actual satellite trajectories, with a nominal drift of approximately 250 meters, result in navigation errors of around 400 meters. Our findings underscore the critical importance of addressing these factors to optimize SoOP navigation performance.

In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a shared pivot station. Initially, we partitioned the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. Subsequently, we computed augmentation parameters for each SN by applying precise orbit corrections and ionosphere-weighted constraints. However, applying the estimated augmentation parameters directly to users across subnetworks encounters challenges due to inter-SN discontinuities. To address this issue, we made an assumption regarding the common pivot station, enabling the formulation’s ability to integrate parameters across SNs. Consequently, the estimated parameters from each SN are integrated and broadcast to users. This architecture offers a reduced computational burden than the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Moreover, the integration process effectively removed the discontinuity of augmentation parameters between SNs, enabling seamless user positioning even when transitioning between SNs. To evaluate the effectiveness of our proposed architecture, we gathered dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORS) in Korea. These data were then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compared the static positioning error and processing time of our proposed architecture with those of the centralized architecture.

This work presents the initial experimentation of a real-time 5G mmWave downlink positioning testbed deployed at Airbus premises. This experimentation is part of a first-of-a-kind testbed for hybrid Global Navigation Satellite Systems (GNSS), 5th Generation (5G) new radio (NR) and sensor positioning, called Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed. The mmWave 5G base station (BS) exploits the 5G standard positioning reference signal (PRS) to support positioning capabilities within the 5G NR downlink transmissions. Outdoor field results are used to characterize the received power levels and beam-based angle-of-arrival (AoA) estimation accuracy of this 5G mmWave PRS platform. The goal is to assess the suitability of this platform to enhance the positioning performance thanks to the 5G downlink mmWave transmissions. To the best of authors knowledge, this paper presents the first AoA results using OpenAirInterface (OAI) PRS mmWave signal transmissions at 27GHz for positioning. These initial field results indicate a maximum coverage of 30 m and the AoA accuracy limited by the reduced number of beams. The limitations and potential enhancements of this platform are provided as future recommendations.

Jamming and Spoofing events are nowadays more and more present over the whole globe. Aircraft avionics and navigation systems that use GNSS can be significantly impacted by these events in many ways, possibly leading to spurious or missed ground proximity warnings, route deviations, loss of approach capability or even risk of misleading approach guidance. It is necessary to raise the awareness of these challenges and provide reliable protection to the avionics systems against the threats.

The GNSS-Inertial integration brings great potential to detect and mitigate the effect of erroneous (spoofed) GNSS data. When GNSS trajectory diverges from (or is inconsistent with) inertial data, the integrated system may detect this erroneous GNSS trajectory and may be able to maintain navigation integrity by rejecting this data.

GNSS Aided Inertial System can provide both self-contained detection of a GNSS spoofing event as well as mitigation, where mitigation is hard to achieve globally with other commercial aviation systems relying on good ground system coverage.

This paper provides an overview of developed Inertial Spoofing Monitor including results of performance validation using simulations. Simulations were performed to evaluate and demonstrate the detection, mitigation, and recovery capability of the spoofing monitor. The performance validation followed the process prescribed by the MOPS RTCA DO-384 in Appendix Q.

In summary, Honeywell is in the process of adding this new patented technology to its inertial products for certification in the near future available as field loadable SW upgrade for most aircraft platforms. It will dramatically improve aircraft resilience to GNSS spoofing and reinforce the value of Honeywell’s GPS-Aided Inertial Systems as the trusted source of navigation.

Autonomous robots are widely used in modern industries and daily life. The global navigation satellite system (GNSS) real-time kinematic (RTK) can provide centimeter-level positioning accuracy in open areas but is significantly challenged in urban canyons, due to the severe signal reflections. To fill this gap, this paper proposed a tightly coupled (TC) GNSS-RTK, inertial navigation system (INS), and odometer (RIO) integration via factor graph optimization (FGO) aided by the GNSS outlier mitigation (see Fig. 1). Different from the existing EKF, the FGO enables the explorations of the correlation within the historical measurements, which leads to improved robustness towards the unexpected outlier measurements. One of the challenges is that the direct integration of the high-frequency INS and odometer measurements can lead to unacceptable computational load using the factor graph model. To cope with the high-frequency raw INS and odometer measurements, the pre-integration is adopted to integrate the high-frequency measurements to maintain computational efficiency. The relevant derivations of the pre-integration model are rigorously formulated together with the Jacobian matrices. Moreover, the marginalization-based sliding window is adopted to achieve real-time performance. To further mitigate the impacts of the potential GNSS outlier measurements, the measurement quality management strategy is proposed by combining the robust estimation and odometer/INS-aided consistency check. Thanks to the accurate relative pose change from IMU and Odometer pre-integration, the system could detect and exclude GNSS outliers in both pseudo-range and carrier-phase measurements, after which ambiguity resolution (AR) is performed with high-quality measurements. To validate the effectiveness of the proposed method, several real-world datasets are collected to validate the proposed method in GNSS-challenging environments. Our experiments showed that our method achieved higher accuracy and consistency than state-of-the-art methods (See Fig. 2). To the best of the author’s knowledge, this is the first work proposing the tightly coupled GNSS-RTK/INS/Odometer integration using FGO.

Global Navigation Satellite Systems (GNSS) have become the primary means of navigation and source of Position,
Navigation and Timing (PNT) information for almost all modes of transport, for general navigation and for timing
purposes. Yet, users operating in urban canyons, or within dense vegetation may suffer from a limited view of the
satellites and multipath conditions, thus encountering difficulties in providing reliable positioning solutions. In such
scenarios, the benefits of Vector Tracking Loops (VTL) in comparison to conventional Scalar Tracking Loops (STLs)
can be exploited to tackle high user dynamics and stringent positioning requirements, thereby countering reflected,
weak or distorted signals. The advantages of VTL help PNT performance by utilising navigation filter outcomes to
compensate for track phase, frequency, and delay offsets. Nevertheless, the VTL is vulnerable to signal degradation
due to its dependency on individual tracking channels. Hence, fusion with an Inertial Measurement Unit (IMU) and
Receiver Autonomous Integrity Monitoring (RAIM) techniques becomes advantageous under the stringent conditions
affected by signal reflection, multipath, and attenuation. To mitigate the challenges of operating under challenging
environments and integrating multiple AI implementations into software-defined GNSS receivers, this paper presents a
new software-defined GNSS architecture incorporated with modern techniques. Specifically, the VTL development
facilitates resilience against Non-Line of Sight (NLOS) effects. The adoption of the Ultra-tightly Coupled (UTC) fusion
mechanism enhances high-dynamic compatibility. The AI feature strengthens detector accuracy and facilitates enhanced
Extended Receiver Autonomous Integrity Monitoring (eERAIM) for fault detection and exclusion. The architecture
performance is evaluated through functionality tests at component level by using a high-fidelity GNSS simulator to
generate synthetic datasets. Using indicators in terms of availability and integrity to assess performance, the proposed
VTL design offers significant advantages, outperforming STL at a cost of increased computational complexity and IMU
integration. Using the integration VTL with AI, our proposition presents the potential to improve resilience in GNSS
receivers.

Electrically charged particles present in this layer of Earth’s atmosphere can alter radio waves, such as those from GPS, Galileo, or BeiDou, resulting in non-estimated errors with respect to the available navigation models for the end user. For most positioning algorithms based in sequential filters, this effect is translated into a slow convergence towards a solution around decimeter error level. If we consider that ionosphere’s effect varies based on the user's location and solar activity due to the atmosphere particle composition, it becomes clear that a global accurate model, valid across wide areas accounting for different seasons and timespans is at the very least, quite challenging.

The focus of this paper is the demonstration of a global ionosphere model designed to improve the positioning accuracy of the end user through the estimation of ionospheric corrections to the broadcasted navigation message. Mathematically, this method is based on a spherical harmonic expansion model. This approach has the advantage of reducing the dependency from a highly densified station network where the ionosphere delay must be constantly estimated in dozens of locations; in favor of a simplified model, that barely needs to be adjusted with a limited set of real time data (around 40 stations). In this case, GMV’s global station network has been used, which comprises geodetic-grade receivers tracking the signal in open-sky locations around the globe. The global ionospheric model is configured to process signals from GPS and Galileo constellations.

To evaluate the performances of this model on the final user position estimation, several Precise Point Positioning (PPP) solutions are computed at different locations. The results have been compared with PPP solutions calculated without ionospheric corrections at the same stations. The goal of this paper is to show the significant performance improvement observed with the implementation of the global model.