No abstract

The ever-increasing public interest in location and positioning services has originated a demand for higher performance global navigation satellite systems (GNSSs). In order to achieve this incremental performance, the estimation of line-of-sight (LOS) delay with high accuracy is a prerequisite for all GNSSs. The delay lock loops (DLLs) and their enhanced variants (i.e., feedback code tracking loops) are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the binary offset carrier (BOC) modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this paper analyzes feedback as well as feedforward code tracking algorithms and proposes the peak tracking (PT) methods, which are combinations of both feedback and feedforward structures and utilize the inherent advantages of both structures. We propose and analyze here two variants of PT algorithm: PT with second-order differentiation (Diff2), and PT with Teager Kaiser (TK) operator, which will be denoted herein as PT(Diff2) and PT(TK), respectively. In addition to the proposal of the PT methods, the authors propose also an improved early-late-slope (IELS) multipath elimination technique which is shown to provide very good mean-time-to-loselock (MTLL) performance. An implementation of a noncoherent multipath estimating delay locked loop (MEDLL) structure is also presented. We also incorporate here an extensive review of the existing feedback and feedforward delay estimation algorithms for direct sequence code division multiple access (DS-CDMA) signals in satellite fading channels, by taking into account the impact of binary phase shift keying (BPSK) as well as the newly proposed BOC modulation, more specifically, sine-BOC(1,1) (SinBOC(1,1)), selected for Galileo open service (OS) signal. The state-of-art algorithms are compared, via simulations, with the proposed algorithms. The main focus in the performance comparison of the algorithms is on the closely spaced multipath scenario, since this situation is the most challenging for estimating LOS component with high accuracy in positioning applications.

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.

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.

More and more satellites are populating the sky nowadays in the Low Earth orbits (LEO). Most of the targeted applications are related to broadband and narrowband communications, Earth observation, synthetic aperture radar, and internet-of-Things (IoT) connectivity. In addition to these targeted applications, there is yet-to-be-harnessed potential for LEO and positioning, navigation, and timing (PNT) systems, or what is nowadays referred to as LEO-PNT. No commercial LEO-PNT solutions currently exist and there is no unified research on LEO-PNT concepts. Our survey aims to fill the gaps in knowledge regarding what a LEO-PNT system entails, its technical design steps and challenges, what physical layer parameters are viable solutions, what tools can be used for a LEO-PNT design (e.g., optimisation steps, hardware and software simulators, etc.), the existing models of wireless channels for satellite-toground and ground-to-satellite propagation, and the commercial prospects of a future LEO-PNT system. A comprehensive and multidisciplinary survey is provided by a team of authors with complementary expertise in wireless communications, signal processing, navigation and tracking, physics, machine learning, Earth observation, remote sensing, digital economy, and business models.