Global Navigation Satellite Systems (GNSS) have become a critical service and this has increased the need for GNSS situational awareness. On top of this, the field is rapidly changing with interference becoming more prominent and new GNSS services being developed, highlighting the fact that flexibility and adaptability are needed from the GNSS monitoring systems. With the emergence of new Galileo features, such as the Open Service Navigation Message Authentication (OSNMA) and the High Accuracy Service (HAS), monitoring systems have the opportunity to leverage these new services to enhance GNSS situational awareness. The Finnish Geospatial Research Institute (FGI) has developed an open GNSS situational awareness service called GNSS-Finland, available at https://gnss-finland.nls.fi. GNSS-Finland monitors the signal quality, detects potential interference, and informs the users of the expected level of performance of different services around 47 stations of the Finnish Continuously Operating Reference Station (CORS) network FinnRef. Recently the GNSS-Finland has been extended with capabilities to monitor and leverage the OSNMA and the HAS around the FinnRef stations. Due to the novelty of both the OSNMA and the HAS, custom software solutions were needed to integrate these services into GNSS-Finland. We will give an overview of GNSS-Finland and its flexible architecture and the integration of the new Galileo services into GNSS-Finland, and finally we discuss from the monitoring system point of view how these new services can be leveraged.

Global Navigation Satellite Systems (GNSS) are starting to offer freely available firsthand services to sustain user needs on accuracy, reliability and resilience in navigation. Examples of these services are the Galileo High Accuracy Service (HAS), in its initial phase since 24^th January 2023, or the Galileo Open Service Navigation Message Authentication (OSNMA). These services are extending not only the consolidated GNSS capabilities but also the perimeter of the system support, providing reference algorithms, guidelines, and standards to be implemented by the users to achieve the service goals. The Joint Research Centre (JRC) supported the HAS development in different phases. Currently, JRC has developed a monitoring system able to collect and analyze HAS corrections broadcast through the Galileo E6-B signal-in-space (SIS) and the Internet Data Distribution (IDD) channels. The corrections are collected and logged in the EU GNSS Hub of the JRC. The monitoring tool combines the SIS and IDD HAS corrections with the OS broadcast ephemeris of the Multi-GNSS Experiment (MGEX). HAS-based satellite positions and clock errors are compared against MGEX Rapid and Final products. Moreover, the position, velocity and timing (PVT) solutions obtained with the reference HAS User Algorithm are computed daily using GNSS measurements from a set of IGS receivers, worldwide distributed to ensure geographical coverage and a hardware in the loop solution developed at the JRC premises, which also allows the collection and analysis of SIS HAS corrections.

In his contribution a set of performance indicators are computed including HAS ephemeris errors and PVT error. Finally, different HAS UA configurations, including dual- and triple-frequency in single (Galileo) and dual (Galileo+GPS) constellation mode, are presented. The file logs of the SIS and IDD HAS corrections, their streams and the experimental HAS ephemeris, clock and bias collected and elaborated at the JRC are stored and made available for research.

Smartphone positioning is a leading GNSS research area and achieving precise positioning is highly coveted. The main roadblock to it is the erroneous Smartphone’s carrier-phase data which is highly prone to cycle slips (CS). There is a dearth of research on Cycle Slip Detection and Repair (CSDR) methods for Smartphone GNSS data. Existing literature for CSDR methods is based on carrier-phase data captured by professional grade receivers, these methods can be broadly categorized into two groups (1) Geometry-Free CSDR (GF-CSDR) and (2) Geometry-Based CSDR (GB-CSDR). It can be understood that GF-CSDR methods rely on individual satellite measurements or their linear combinations to detect CS using only single-channel data processing. On the flip side, the GB-CSDR technique performs multi-channel processing, considering the satellite-receiver geometry, which significantly affects the precision of CSDR. The proposed method will be a real-time CSDR method combining both GF and GB-CSDR. The GF-CSDR part will entail implementing a dedicated CS monitor for individual satellite’s phase data, based on the principle that the difference between the delta range derived from carrier-phase and Doppler measurements from two consecutive epochs should not exceed a certain threshold given no CS occurs. Then a GB-CSDR method based on statistical testing of the innovation sequence will be further performed as a safeguard. The rationale behind using a Doppler-based GF-CSDR rather than using other common GF-CSDR combinations e.g. Geometry Free (GF), Melbourne-Wubenna (MW), Phase-minus-Code (PMC) is that other combinations require either dual-frequency data or pseudorange measurement. Since Smartphone pseudorange data is quite noisy, its Doppler data provides a great alternative as it is comparatively precise. Additionally, the GB-CSDR method is augmented using the Doppler-based prediction technique, introduced in our previous work. The proposed algorithm’s flowchart along with preliminary results for the GF-CSDR part are shown in the provided figures.

Resilience of Global Navigation Satellite System (GNSS) usage against spoofing attacks can be increased by signal monitoring algorithms aiming to detect a spoofing signal at the acquisition stage of GNSS receiver signal processing. A common approach is to search for the presence of multiple correlation peaks in the absolute value of the Cross-Ambiguity Function (CAF). In this context, it is particularly challenging to detect spoofing signals with a correlation peak closely aligned to that of the authentic signal, as is the case at the early stage of a coherent spoofing attack. In the present work, a spoofing detection method is proposed that monitors the magnitude of the CAF by means of clustering techniques. It is designed to detect the pull-off during a coherent power-matched spoofing attack already at an early stage. The method is evaluated for the GPS L1 C/A signal based on a static scenario of the Texas Spoofing Test Battery (TEXBAT) data set as well as for the Galileo E1-B signal based on a real-world digital snapshot recording in the E1 frequency band which is augmented by emulated spoofing signals at the level of digital signal processing.

In spite of the extensive growth of new positioning solutions experienced in recent years, Global Navigation Satellite Systems (GNSSs) still remain at the core of the navigation technologies. As new use cases emerge, the demands in terms of accuracy and reliability are escalating, and obtaining precise and robust positioning solutions becomes essential for the proper functioning of modern services.

These requirements are not new for certain GNSS sectors, where a high precision positioning has been present for some time. However, bringing these achievements into the mass market entails a hard challenge.

The same happens when robustness is discussed; numerous approaches have been proposed to guarantee the availability of the service, but most of them are far from the mass market due to the complexity or cost. Among the most widespread solutions lies beamforming, which involves deploying multiple antennas at the receiver in order to enhance the desired signals and mitigate undesired contributions that may hinder the performance of the receiver or completely block the service. This approach, broadly employed in fields such as wireless communications, has been entering the GNSS receiver segment during recent years thanks to the miniaturization trends and the cost reduction of the radio-frequency elements, allowing the use of multiple antennas in generic GNSS receivers.

In this work, the potential of beamforming in mass market receivers is analysed using a five-channel low-cost software defined radio (SDR), KrakenSDR, showing the capabilities that conventional and low-complexity spatial diversity techniques provide in harsh scenarios. Lab verification tests using Spirent simulator have been performed to validate the implemented techniques in a software GNSS receiver. The results obtained show a positioning error of below ten meters in the presence of an interference that, without further action taken, would not allow the receiver to acquire the signal.

Nowadays, ultra-large container vessels (ULCVs) can have a beam of up to 60 meters, which contributes to their high stability and short roll periods. The loss of containers on ULCVs can have various causes, often due to inadequate container securing gear, container stability, weather conditions and ship navigation. Around 7000 containers, according to World Shipping Council, have been lost at sea in the last three years, although the number of unreported cases is even higher. To tackle this problem, research produced alternative approaches. However, from a navigational perspective, it is not possible to completely prevent container losses with these. The Container Tracking and Accident Detection (ConTAD) project presented in this article, which is funded by the German Federal Ministry for Economic Affairs and Climate Action, therefore takes a reactive approach.

The aim of the project is to increase the safety of shipping, strengthen supply chains, improve the salvage of containers, and increase the sustainability of shipping in European waters and beyond by further developing container tracking technology to detect container losses at sea. The project will utilize industry standards in communication and navigation technology such as GNSS, AIS and ECDIS. This paper describes the basis for the advanced container tracking technology to be developed based on a real container loss incident.

To this end, the incident involving the container ship MSC Zoe, which lost 342 containers off the Dutch coast on the night of January 1 to 2, 2019, is analyzed. It then looks at ways in which container tracking technology can be further developed. The following section provides an overview of current container tracking technology and monitoring systems and highlights the current technological gaps. Finally, it discusses what technologies need to be further developed to reach the project’s goals.

Flare Bright has developed a patent pending, Machine Learning (ML) enhanced software system that boosts the performance of inexpensive Inertial Measurement Units (IMU), resulting in a degree of accuracy that creates an effective low cost, low weight and low volume Inertial Navigation System (INS) for extended flight operations of unmanned aerial systems (UAS) in GNSS-denied environments.

This enhancement can give highly accurate position data when GNSS fails, adding redundancy to flight safety critical systems, and is widely applicable within integrated navigation performance management systems on a wide range of drones and aircraft.

Flare Bright has previously presented preliminary flight tests results of this GNSS-free capability gathered using a 1.2m wingspan in-house fixed wing drone at ENC2023. In this paper, Flare Bright will present new data gathered by deploying our solution in a realistic operational scenario using a representative 2m wingspan fixed wing operational drone over multiple terrains, including over water where visual navigation is not possible (Fig 1).

The results, from flights up-to 1 hour long, will show how Flare Bright’s model-based navigation software enables a $5 smartphone IMU to outperform a $10k tactical grade IMU (HG1930) in approximately 20 minutes, and results in an average performance approximately 4 times the error of the $100k navigation gold standard IMU HG9900 after 1 hour (Fig 2).

Flight test data will be analysed in conjunction with simulation data to provide a critical review of the constraints and potential capability achievable with this novel technique. The results presented will demonstrate a credible route towards a practical, operational, future capability within the emerging UAS sector and the potential value of using mass-market sensors with software enhancements within the wider aviation sector where safety is paramount.

This paper discusses flight test results of a precise relative navigation and separation assurance system based on the exchange of Global Navigation Satellite (GNSS) measurements via new message types for the Automatic Dependent Surveillance – Light (ADB-L) instead of Broadcast (ADS-B). With an increased use of Unmanned Aircraft Systems (UAS) for a wide variety of applications, such as law enforcement, search and rescue, agriculture, infrastructure inspection, maintenance, mapping, and journalism, it is expected that UAS will be taking off, landing, or otherwise operating at airfields at the same time as manned aircraft. These operations may lead to smaller separation between the participants and, thus, higher collision risk. To enable smaller separation, relative position and velocity estimators are required that can meet stricter navigation performance. Typically, ADS-B transmits traffic position and velocity estimates output by the onboard GNSS receiver. A measurement-based ADS-B implementation, which transmits raw measurements from the GNSS receiver rather than aircraft state vectors and performance parameters, has been proposed in previous papers by the authors to improve surveillance performance and add integrity to the surveillance solution. Test results of the proposed method have shown meter-level relative position accuracy and millimeter-per-second-level relative velocity accuracy. This paper will review these methods and their performance, propose an implementation on ADS-L, discuss how this level of performance can enable the simultaneous operation of manned and unmanned aircraft at low altitudes in the vicinity of airfields, and show how off-nominal operations can be detected (e.g., leaving the geofence or deviations from approved trajectories). The paper will furthermore discuss recent flight tests with one manned aircraft and two UAS as part of project Safefly and illustrate the benefits of using GNSS measurement-based ADS-L data for separation assurance as opposed to traditional methods.

Spoofing attacks toward Global Navigation Satellite System(GNSS) users aim at falsifying the position, velocity, and timing(PVT) results, and are therefore recognized as a security threat with severe consequences. As countermeasures, various anti-spoofing techniques are proposed to detect, recognize, and eliminate spoofing signals. A typical series of anti-spoofing techniques utilize the spatial characteristics of the spoofing signals. They share a common hypothesis that spoofing signals are transmitted by a single antenna and thus have identical propagation paths. These techniques can provide effective safeguards when the single antenna hypothesis is satisfied.

Yet apparently, spoofing signals can potentially be transmitted from distributed directions, with multiple antennas each transmitting a single signal. We call it distributed GNSS spoofing. Intuitively, distributed GNSS spoofing is likely to disable the above spatial-characteristic-based anti-spoofing techniques. However, it is hard to verify the above inference. Up to now as far as we know, distributed GNSS spoofing threat remains a theoretical possibility and has not been validated by any published literature.

To validate the distributed GNSS spoofing threat and supplement a new GNSS security assessment instrument, we develop a spoofer for distributed GNSS spoofing using FPGA platform. It can generate spoofing signals from a maximum of eight independent output ports. With our spoofer, we construct an experiment platform to explore the influence of distributed GNSS spoofing. We will present the experiment results and analysis from the following three aspects.

1) Testing and validating the fundamental operation of distributed GNSS spoofing in an indoor environment.

2) Implementing drift-off spoofing using the distributed GNSS spoofing structure in an open-sky environment.

3) Performance of representative spatial-characteristic-based anti-spoofing techniques against distributed GNSS spoofing.

To summarize, our work verifies the feasibility of distributed GNSS spoofing, reveals the limitation of spatial-characteristic-based anti-spoofing techniques, and urges for more sophisticated anti-spoofing techniques.

Drones are increasingly being used for a variety of applications, but their use can be hampered when Global Navigation Satellite Systems (GNSS) signals are unavailable. This paper presents Terrain Referenced Navigation (TRN) from a class 1 drone using low-cost, automotive-class mm-wave radar to enable accurate navigation in GNSS-denied environments. The radar-derived elevation profile of the ground beneath the drone as it flies is compared to a lidar-based digital terrain map and, using a particle filter, the drone’s position is estimated. Results show that mm-wave radar TRN can provide an accurate navigation capability in the absence of GNSS and offers several advantages over other alternative navigation technologies.