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Vulnerability of satellite-based navigation signals to intentional and unintentional interference calls for a high-level overview of Global Navigation Satellite System (GNSS) threats occurring globally to understand the magnitude and evolution of the problem. Therefore, a mechanism needs to be developed whereby disparate monitoring systems will be capable of contributing to a common entity of basic information about the threat scenarios they experience. This paper begins with a literature survey of 37 state-of-the-art GNSS threat monitoring systems, which have been analysed based on their respective operational features - constellations monitored and whether they possess the capability to perform interference-type classification, spoofing detection, and interference localisation. Also described is a comparative analysis of four GNSS threat reporting formats in use today. Based on these studies, the paper describes the Horizon2020 Standardisation of GNSS Threat Reporting and Receiver Testing through International Knowledge Exchange, Experimentation and Exploitation (STRIKE3) proposed integrated threat monitoring demonstration system and related standardised threat reporting message, to enable a high-level overview of the prevailing international GNSS threat scenarios and its evolution over time.

Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

Global Navigation Satellite System (GNSS)-based positioning is experiencing rapid changes. The existing GPS and the GLONASS systems are being modernized to better serve the current challenging applications under harsh signal conditions. These modernizations include increasing the number of transmission frequencies and changes to the signal components. In addition, the Chinese BeiDou Navigation Satellite system (BDS) and the European Galileo are currently under development for global operation. Therefore, in view of these new upcoming systems the research and development of GNSS receivers has been experiencing a new upsurge. In this article, the authors discuss the main functionalities of a GNSS receiver in view of BDS. While describing the main functionalities of a software-defined BeiDou receiver, the authors also highlight the similarities and differences between the signal characteristics of the BeiDou B1 open service signal and the legacy GPS L1 C/A signal, as in general they both exhibit similar characteristics. In addition, the authors implement a novel acquisition technique for long coherent integration in the presence of NH code modulation in BeiDou D1 signal. Furthermore, a simple phase-preserved coherent integration based acquisition scheme is implemented for BeiDou GEO satellite acquisition. Apart from the above BeiDou-specific implementations, a novel Carrier-to-Noise-density ratio estimation technique is also implemented in the software receiver, which does not necessarily require bit synchronization prior to estimation. Finally, the authors present a BeiDou-only position fix with the implemented software-defined BeiDou receiver considering all three satellite constellations from BDS. In addition, a true multi-GNSS position fix with GPS and BDS systems is also presented while comparing their performances for a static stand-alone code phase-based positioning.

Global navigation satellite systems (GNSSs) have been experiencing a rapid growth in recent years with the inclusion of Galileo and BeiDou navigation satellite systems. The existing GPS and GLONASS systems are also being modernized to better serve the current challenging applications under harsh signal conditions. Therefore, the research and development of GNSS receivers have been experiencing a new upsurge in view of multi-GNSS constellations. In this article, a multi-GNSS receiver design is presented in various processing stages for three different GNSS systems, namely, GPS, Galileo, and the Chinese BeiDou navigation satellite system (BDS). The developed multi-GNSS software-defined receiver performance is analyzed with real static data and utilizing a hardware signal simulator. The performance analysis is carried out for each individual system, and it is then compared against each possible multi-GNSS combination. The true multi-GNSS benefits are also highlighted via an urban scenario test carried out with the hardware signal simulator. In open sky tests, the horizontal 50 % error is approximately 3 m for GPS only, 1.8 to 2.8 m for combinations of any two systems, and 1.4 m when using GPS, Galileo, and BDS satellites. The vertical 50 % error reduces from 4.6 to 3.9 when using all the three systems compared to GPS only. In severe urban canyons, the position error for GPS only can be more than ten times larger, and the solution availability can be less than half of the availability for a multi-GNSS solution.

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This paper demonstrates the effect of radio frequency (RF) front-end (FE) free-running local oscillator (FRO) phase noise (PN) on the phase component of the Global Navigation Satellite System (GNSS) code correlation product. It is observed that as FE PN increases, it adversely affects the stability of the phase component of the code correlation. The tracking loops in baseband processing of a GNSS receiver attempt to lock on to the frequency, delay and phase of the correlation product. Until these parameters are varying within acceptable bounds, set by the dynamics handling capability of the tracking loops, the tracking loops are able to successfully track the satellite signal. However, PN increases the variation in phase of the correlation product calculated over consecutive epochs and may also cause loss of tracking lock if these variations go beyond phase locked loop (PLL) pull-in range thresholds. This paper studies the relation between FRO PN and phase component of correlation through numerical analysis, and software simulations by artificially contaminating GNSS signal stream with PN of increasing variance and checking the result on the standard deviation (SD) of the phase component of correlation product. Based on these results, this paper recommends certain maximum limits on the FE PN in order to keep the SD of phase component below the onesigma phase error limits of the PLL used in typical GNSS tracking loops.

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