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Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 frequency jammer. A suitable area for testing was established in Slovenia. Nine receivers from different manufacturers were under consideration in this study. While positioning, intentional 3-minute jammings were performed by a jammer that was located statically at different distances from receivers. Furthermore, kinematic disturbances were performed using a jammer placed in a vehicle that passed the testing area at various speeds. An analysis of different scenarios indicated that despite the use of an L1/E1 jammer, the GLONASS (Russian: Globalnaya Navigatsionnaya Sputnikovaya Sistema) and Galileo signals were also affected, either due to the increased carrier-to-noise-ratio (C/N0) or, in the worst cases, by a loss-of-signal. A jammer could substantially affect the position, either with a lack of any practical solution or even with a wrong position. Maximal errors in the carrier-phase positions, which should be considered a concern for geodesy, differed by a few metres from the exact solution. The factor that completely disabled the signal reception was the proximity of a jammer, regardless of its static or kinematic mode.

Jamming is becoming a serious threat to various users of global navigation satellite systems (GNSS). Therefore, live monitoring tests are required to estimate the sensitivity range of GNSS receivers under jamming. This study analyses the response of some mass-market and professional-grade receivers to intentional interferences based on different 3D jammer positions. First, the vertical jamming was investigated, followed by a horizontal experiment where the receivers were placed at three locations while the jammer was moving within a triangular area. The aim was to determine a fingerprint of the influence of the L1/E1 chirp jammer on receivers used in the research. The results show that low-cost receivers are much more susceptible to interference, while the latest generation of GNSS geodetic receivers are much more resilient. It is encouraging that positioning in the presence of jamming could be achieved on a larger scale, especially by using professional receivers. An attempt to position the jammer will be left for trials when a more frequency stable device is applied.

Smartphones now dominate the Global Navigation Satellite System (GNSS) devices capable of collecting raw data. However, they also offer valuable research opportunities in intentional jamming, which has become a serious threat to the GNSS. Smartphones have the potential to locate jammers, but their robustness and sensitivity range need to be investigated first. In this study, the response of smartphones with dual-frequency, multi-constellation reception capability, namely, a Xiaomi Mi8, a Xiaomi 11T, a Samsung Galaxy S20, and a Huawei P40, to various single- and multi-frequency jammers is investigated. The two-day jamming experiments were conducted in a remote area with minimal impact on users, using these smartphones and two Leica GS18 and two Leica GS15 geodetic receivers, which were placed statically at the side of a road and in a line, approximately 10 m apart. A vehicle with jammers installed passed them several times at a constant speed. In one scenario, a person carrying the jammer was constantly tracked using a tacheometer to determine the exact distance to the receivers for each time stamp. The aim was, first, to determine the effects of the various jammers on the smartphones’ positioning capabilities and to compare their response in terms of the speed and quality of repositioning with professional geodetic receivers. Second, a method was developed to determine the position of the interference source by varying the signal loss threshold and the recovery time on the smartphone and the decaying carrier-to-noise ratio (CNR). The results indicate that GNSS observations from smartphones have an advantage over geodetic receivers in terms of localizing jammers because they do not lose the signal near the source of the jamming, but they are characterized by sudden drops in the CNR.

Robust autonomous driving, as long as it relies on satellite-based positioning, requires carrier-phase-based algorithms, among other types of data sources, to obtain precise and true positions, which is also primarily true for the use of GNSS geodetic receivers, but also increasingly true for mass-market devices. The experiment was conducted under line-of-sight conditions on a straight road during a period of no traffic. The receivers were positioned on the roof of a car travelling at low speed in the presence of a static jammer, while kinematic relative positioning was performed with the static reference base receiver. Interference mitigation techniques in the GNSS receivers used, which were unknown to the authors, were compared using (a) the observed carrier-to-noise power spectral density ratio as an indication of the receivers’ ability to improve signal quality, and (b) the post-processed position solutions based on RINEX-formatted data. The observed carrier-to-noise density generally exerts the expected dependencies and leaves space for comparisons of applied processing abilities in the receivers, while conclusions on the output data results comparison are limited due to the non-synchronized clocks of the receivers. According to our current and previous results, none of the GNSS receivers used in the experiments employs an effective type of complete mitigation technique adapted to the chirp jammer.

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.

The purpose of this study is to analyse data from the marine pilots’ bio-sensor readings to determine how experience affects their biometrical response during the port approach. The experiences play a significant role in the participant’s decision-making process and correlate with the repetitions. Through the repetitions of the experimental task, the participants gain experience, which correlates with the biometrical response, e.g., heart rate, electrodermal activity, etc. After exposing the two experience-distinct groups of participants to the same simulated port-approaching task, their collected biometric data is analysed and discussed. The results show that biometrical readings of the less experienced participants typically vary compared to that of the experienced participants, who take the simulated task more seriously. The study also yields insight into the workload process, involving disturbing factors during the task.

This paper presents a unique 200-day dataset of raw GNSS single-frequency L1 receiver measurements collected aboard a research vessel during a continuous Antarctic expedition from October 7, 2024, to April 24, 2025. The dataset comprises daily-segmented binary UBX files containing multi-constellation L1-band observations, including position and time (UBX-NAV-PVT), satellite metadata (UBX-NAV-SAT), RF front-end diagnostics (UBX-MON-RF), FFT results of the received signal spectrum (UBX-MON-SPAN), raw satellite pseudorange and carrier-phase data (UBX-RXM-MEASX), and intermittent Galileo SAR return-link messages (UBX-RXM-RLM). Files are organized by UTC date to streamline batch parsing and maintain file sizes under  ≈ 600 MB. Synchronized receiver state metrics and high-resolution spectrum waterfalls allow for a detailed characterization of multipath propagation, the consequences of unintentional interference from ground systems, and intentional jamming conditions in all types of maritime environments, from Mediterranean to polar regions. This dataset facilitates GNSS performance modeling, enables diverse radio-frequency interference analysis, and allows for antenna performance characterization in harsh marine environments. Furthermore, it aids in resilient PNT algorithm development for autonomous vessels and assists in benchmarking Real-Time Kinematic (RTK) positioning in low signal-to-noise and dynamic motion scenarios.

We investigate the identification of mobile phones through their built-in magnetometers. These electronic components have started to be widely deployed in mass market phones in recent years, and they can be exploited to uniquely identify mobile phones due their physical differences, which appear in the digital output generated by them. This is similar to approaches reported in the literature for other components of the mobile phone, including the digital camera, the microphones or their RF transmission components. In this paper, the identification is performed through an inexpensive device made up of a platform that rotates the mobile phone under test and a fixed magnet positioned on the edge of the rotating platform. When the mobile phone passes in front of the fixed magnet, the built-in magnetometer is stimulated, and its digital output is recorded and analyzed. For each mobile phone, the experiment is repeated over six different days to ensure consistency in the results. A total of 10 phones of different brands and models or of the same model were used in our experiment. The digital output from the magnetometers is synchronized and correlated, and statistical features are extracted to generate a fingerprint of the built-in magnetometer and, consequently, of the mobile phone. A SVM machine learning algorithm is used to classify the mobile phones on the basis of the extracted statistical features. Our results show that inter-model classification (i.e., different models and brands classification) is possible with great accuracy, but intra-model (i.e., phones with different serial numbers and same model) classification is more challenging, the resulting accuracy being just slightly above random choice.

This paper is intended to give an overview of the experiments to evaluate the cognitive load of the officer on watch (OOW) during a collision avoidance maneuver in a full-mission simulator. The main goal is to investigate the possibilities of recording the biometric parameters of an OOW during a simulated collision avoidance maneuver. Potentially dangerous navigation errors known as human erroneous action (HEA) are induced by excessive cognitive load. Despite modern navigational aids on the ship’s bridge, investigators of maritime incidents typically link the reason for incidents at sea with human factors, including high cognitive load. During the experimental tasks on the bridge, the biometric parameters of the OOW are recorded. Statistical tools are used to visualize the data and evaluate the cognitive load of the OOW. Biometric peaks of the OOW typically occur either during the collision avoidance maneuver or when the OOW has been exposed to disturbing factors that increase reaction time and cause potentially dangerous navigation. Assessing the cognitive load of OOWs in the simulator is challenging for several reasons: e.g., the environmental conditions of the simulator, the type of task to be simulated, and even the type of sensor used. After careful study of the available literature, an original experimental design using non-invasive biometric sensors is proposed.

The berthing of an ultra large ship is always a difficult issue and becomes yet more complex when vessels must be handled in restricted manoeuvring areas of limited depth, exposed to a forceful crosswind, or manoeuvring in a strong current, or all three. The final approaching manoeuvre and precise positioning is particularly demanding at container terminals where many STS cranes are located along the quay, seriously limiting margin for error in the process of mooring a ship, especially when the cranes are located nearby a bridge wing or at the very edge of the pier. In order to avoid collisions, the final manoeuvre (side-push) must be fully controlled; the ship’s orientation must be parallel with the quay while maintaining the minimum lateral approaching velocity without significantly shifting the vessel longitudinally. The mooring of a Ro-Ro vessel is occasionally even more challenging: a precise docking manoeuvre is normally executed without any towing assistance. In this paper low cost laser-based berthing and docking systems developed for the ports of Koper and Swinousce are presented and several berthing manoeuvres are analysed and compared with the most commonly used GNSS-based navigational aid system portable pilot units (PPU).