The emerging development of Global Navigation Satellite System (GNSS) software receivers has opened new opportunities in diverse operations. However, Non-Line-of-Sight (NLOS) concatenated signal reception is one prevalent deterioration factor causing positioning errors in urban scenarios. To enhance integrity and reliability through Receiver Autonomous Integrity Monitoring (RAIM) techniques in urban environments, distinguishing between Line-of-Sight (LOS) and NLOS signals facilitates the exclusion of NLOS channels: this is challenging due to uncertain signal reflections/refractions from diverse obstruction conditions in the built environment. Moreover, NLOS features show similarity to multipath effects like scattering and diffraction which brings difficulty in identifying the NLOS type. Recent work exploited NLOS detections with multi-correlator outputs using neural networks that outperform using signal strength techniques for NLOS detection. This paper proposes a novel neural network approach designed to recognise and learn spatial features among early, late, and prompt correlator outputs, differentiating between correlations, and also by memorising temporal features to acquire propagation information. Specifically, the spatial features of correlator IQ streams are derived from convolutional layers incorporated with concatenations, to formulate associate models like early-minus-late discrimination. A Recurrent Neural Network (RNN), i.e. Long Short-term Memory (LSTM) is integrated to obtain comprehensive temporal features; hereby, a softmax classifier is appended in the last layer to distinguish between NLOS and LOS signals. By simulating synthetic datasets generated by a Spirent simulator and captured by a software-defined radio (SDR), the correlator outputs are acquired during the scalar tracking stage. The product of the proposed network demonstrates high performance in terms of accuracy, time consumption and sensitivity, affirming the efficiency of utilising early-stage correlations for NLOS detection. Moreover, an impact analysis of varying the sliding window length on NLOS discrimination underscores the need to fine-tune the parameter, as well as balancing accuracy, operation complexity and sensitivity.

Since 2022, DLR’s Mobile Rocket Base (MORABA) has observed jamming of GNSS signals on sounding rockets launched from Esrange in northern Sweden and Andøya rocket range in northern Norway. Within these flights, different types of single-frequency and single- or multi-constellation GNSS receivers were used.

The jamming primarily affected the GPS L1, Galileo E1 and BeiDou B1C and B1I signals on the L1 frequency band and was noticeable through a pronounced reduction of the carrier-to-noise ratio of the received GNSS signals. Jamming was observed in northern Sweden at an altitude above 25 km and in northern Norway at an altitude above 36 km. Geometric considerations made it possible to roughly localise the source of the jamming signals from the points of the flight path marking the start and end of interference.

The MAIUS-2 sounding rocket, which was launched from Esrange in December 2023, was equipped with an advanced GNSS receiver that was able to analyse the power spectrum of the GNSS signals online. This made it possible to identify the frequencies and bandwidths of the jamming signals on the L1 frequency band.

The flight guidance and range safety of future micro launch vehicles to be launched from the new spaceports in Sweden and Norway, may be affected by the GNSS jamming if their navigation systems overly rely on the availability and quality of a GNSS navigation solution. Manufacturers and operators should be aware of the possible presence of intentional GNSS jamming in northern Scandinavia and the risk of losing or degrading the GNSS navigation solution during parts of the critical guided flight phase.

The paper summarises the GNSS jamming events observed on the recent sounding rocket flights, presents a geometric analysis for identifying the approximate jammer location, and shows the results of the power spectrum analysis on the MAIUS-2 sounding rocket.

The concept of Global Navigation Satellite System (GNSS) meta-signal, which relies on the coherent processing of two components broadcast on different frequencies, is gaining significant interest in modern receivers. The resulting meta-signal has a wide Gabor bandwidth leading to high-accuracy pseudoranges and consequently high-accuracy code solutions. An example of GNSS meta-signal is the Galileo Alt-BOC, which combines the E5a and E5b side-band components.

Recent research work has shown that accurate Galileo Alt-BOC pseudoranges can be produced through the synthetic meta-signal reconstruction approach, where high-accuracy pseudoranges are obtained as the combination of side-band code measurements smoothed using the side-band wide-lane linear Carrier Phase (CP) combination.

For high-end receivers, the benefits of high accuracy pseudoranges have been demonstrated. However, in mass-market devices outliers can be present in the reconstructed measurements. Moreover, the original reconstruction approach obtained meta-signal CPs as the average of the side-band CPs hence losing the CP integer nature. This paper extends the applicability of the meta-signal synthetic reconstruction approach by introducing a Half-Cycle Ambiguity Resolution (HCAR) rule for the meta-signal CPs and a pseudorange jump detect-and-recover method to improve synthetic pseudoranges continuity.

The proposed approach has been implemented on the STMicroelectronics TeseoV, triple band multi-constellation receiver able to track E1, E5a and E5b, and represents an example of the application of the synthetic measurement reconstruction meta-signal paradigm. This serves as a special feature in automotive devices where the limited front-end bandwidth and hardware correlation constraints do not allow for direct full Alt-BOC tracking.
Analysis has been conducted considering zero baseline experiments where a professional receiver was used as reference and static tests in the position domain. The experimental results show that the extended synthetic meta-signal approach is effective to produce Galileo Alt-BOC observables whose quality is comparable with that of measurements produced by professional devices implementing full-band Alt-BOC processing.

The Ultra-Wide Band (UWB) Real-Time Localization Systems (RTLS) usually require high position-fix rates and therefore cannot rely on the conventional but slow Two-way ranging (TWR) approach. Some of the RTLS utilize Time (Difference) of Arival (ToA or TDoA) positioning, in order to achieve hundreds or thousands positioning fixes per second. However, such systems require either precise and robust synchronization of the anchor transceivers, or sufficiently stable clocks for methods referred to as “synchronization-free”. Usually, reasonably priced crystals or TCXOs are utilized as frequency references for the UWB transceivers.

Clock characterization and simulation is necessary to evaluate and tune the synchronization or positioning algorithms without the need of hardware-pulling of the UWB reference oscillators. The simulation also allows testing of the algorithms with defined and repeatable scenarios, potentially featuring extreme clock states, possibly beyond the specified operational envelope.

We present the methodology of evaluation of the UWB timebase characteristics from the transmission and reception timestamps readily available in the widely used Qorvo DW1000 UWB transceiver. The analysis is based on modified Allan Deviation (MDEV) metric; Fig. 1 shows previously acquired data for a pair of DWM1000 modules. Also, Matlab-based timestamp simulation that benefits from the clock characterization will be presented.

Several transceiver pairs will be subject to timebase stability evaluation, including EVB1000 Evaluation board, DWM1000 module with integrated reference oscillator and DW1000 referenced to Caesium standard. A White-Rabbit synchronization will be used to obtain a 10MHz reference signal from the HP 5071A Cs-Clock, the necessary 38.4MHz reference for the DW1000 will be derived using a suitable RF generator with external reference (see Fig. 2).

Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is an evolution of RAIM, developed to exploit the benefits of dual-frequency, multi-constellation. The ARAIM concept was initially developed to serve the aviation community. The European Commission (EC) is looking into the exploitation of the concept by other sectors. This paper focuses on the modelling of the local errors, in order to use of the ARAIM concept for three specific sectors: rail, maritime and UAVs.

This paper further develops the high-level evolutions proposed in the previous project ARAIMTOO for the three sectors:

• _{For Rail, combination of SBAS with ARAIM, plus hybridization with IMU/Odometer.}
• _{For Maritime, an ARAIM solution based on multiple antenna processing in combination with SBAS.}
• _{For UAVs, combination of PPP with ARAIM plus hybridization with IMU.}

Aspects relative to integrity concept, including the definition of the architecture and the user algorithm, as well as a proposed tailoring of the Integrity Support Message, will be discussed. It then focuses on the characterization of the local effect models for multipath and Non-Line-Of-Sight (NLOS), by means of real data campaigns. In this respect, the Experimentation described in the paper includes results from the processing of GNSS real data. Finally, future work on the use of these models to perform the proof-of-concept of the proposed ARAIM evolutions will be discussed.

As will be shown in this paper, accurate and reliable local error models can enable the use of ARAIM beyond aviation, meeting the requirements of different applications within the Rail, Maritime and UAVs sectors.

The work is performed in the frame of the Horizon Europe project ARAIMFUSE (Advanced RAIM for Rail, Maritime and UAS) project, led by GMV and funded by EC.

WiFi is one of the primary tools for mobile indoor positioning due to its vast infrastructure presence. WiFi Fine Time Measurement (FTM) [1], is a WiFi protocol that enables the time of flight (ToF) of a WiFi signal to be determined. The system that applies this protocol is commonly referred to as WiFi Round Trip Timing (RTT). This enables more accurate and reliable indoor positioning than WiFi Residual Signal Strength Indicator (RSSI)-based positioning. A core problem of indoor positioning is prior knowledge of the environment. With current WiFi-based positioning a large database of landmark/access point (AP) positions and/or RSSI fingerprints is required. This paper provides a solution to this problem by harnessing a simultaneous localisation and mapping (SLAM) algorithm [2], using WiFi RTT, and pedestrian dead reckoning, which uses the inertial sensors in the smartphone . This incorporates prior work from [3] that developed filters and outlier detection tailored to WiFi RTT-based positioning and builds upon previous research of WiFi-RTT SLAM from Gentner et al. [4].

The research in this paper demonstrates WiFi-RTT SLAM across 8 different scenarios. The algorithms have no prior knowledge of the environment, other than an estimate of the mobile device location and heading at the start of the scenario. For the longest trial, which was 35 steps long, shown in Figure 1, the average position Root Mean Square Error (RMSE) across all steps throughout the movement of the pedestrian was 865mm and the final positioning error was 657mm. These results are sufficient for indoor positioning as a pedestrians path can be followed and accurately positioned with sub-metre accuracy. Figure 2 shows the positioning RMSE per step for the same trial. The maximum positioning error is 2823mm. The results of this paper show that unmapped indoor positioning using WiFi RTT is feasible for sub-metre indoor positioning.

Global Navigation Satellite Systems (GNSS) reference receivers are an essential part of ground stations that make the operation of Satellite-Based Augmentation Systems (SBAS) possible. In the recent years, there has been an increasing concern on the presence of spoofing and interference events, which have been shown to seriously threaten the operation of GNSS receivers. While this concern is transversal to all GNSS receivers, it stands with particular emphasis in those deployed in the framework of liability- and safety-critical applications, as it is the case of GNSS reference receivers in SBAS ground stations. It is for this reason that dedicated countermeasures are needed in order to preserve the reliability of the observables provided by such receivers.

It is well-known, though, that no single countermeasure is capable of succeeding in the problem of detecting the abovementioned threats in all possible working conditions. Instead, a plurality of techniques is needed in order to cover all the different features and behaviors that can be experienced by a GNSS receiver under attack.

In this context, the goal of this paper is two-fold. On the one hand, it presents a set of spoofing and interference detection techniques that are specifically tailored to operate with the output observables provided by a NovAtel G-III reference receiver. On the other hand, these techniques are assessed using a conducted test with a Safran Skydel GSG-8 GNSS RF simulator in order to validate their implementation and effectiveness. To this end, a set of dedicated spoofing and interference scenarios have been implemented to specifically assess the goodness of the proposed detection techniques. The work concludes with the analysis of the obtained extensive results, as well as it provides insightful recommendations and guidelines.

With the renewed interest in the Moon, manifested by the growing number of planned
missions for the past decade, space agencies are investing in reliable and dedicated lunar communication
and navigation systems and services, such as the Moonlight programme of the European
Space Agency (ESA), to provide support to all types of lunar users (i.e., surface users, landers and
orbiters). In the context of lunar human and robotic exploration, one of the critical phases will be the
landing of spacecrafts on the lunar soil. This type of operation is far from trivial, as it was shown by
the recent crashes such as the one of the Luna25 lander from the Russian Space Agency. A reliable
method to position a lander during its descent could be provided by a dedicated lunar navigation
system. This paper will focus on what is the achievable positioning accuracy for a lander landing on
the Moon’s South Pole using of dedicated satellite-based navigation services such as Moonlight. It
will be shown that using the LCNS constellation and the altimeter can achieve a sub 50m accuracy
with a 99% percentile confidence interval.

We are working on an innovative approach to outdoor human motion capture, using a wearable device that uses integration of low cost GNSS (Global Navigation Satellite System) receiver and INS (Inertial Navigation System) with Zero-Velocity Update (ZUPT) methodology.In this study, we focused on using these devices to reconstruct the foot trajectory. Our work addresses the challenge of capturing precise foot movements in uncontrolled outdoor environments, a task traditionally constrained by the limitations of laboratory settings. We mounted these devices that combine inertial measurement units (IMUs) with GNSS receivers in this configuration: one on each foot and one on the head. We experimented with different GNSS data processing techniques, such as Real-Time Kinematic (RTK) positioning with and without Moving Base, and after the integration with the IMU, we obtained centimeter-level precision in horizontal positioning for various walking speeds. This integration leverages a loosely coupled GNSS/INS approach, where the GNSS solution is independently processed and subsequently used to refine the INS outputs. Enhanced by ZUPT and Madgwick filter this method significantly improves the trajectory reconstruction accuracy, particularly in horizontal positioning, while revealing limitations in vertical component accuracy and high sensitivity to walking speed. Indeed our research includes a study of the impact of moving speed on the performance of these low-cost GNSS receivers. These insights pave the way for future exploration into tightly-coupled GNSS/INS integration using low-cost GNSS receivers, promising advancements in fields like sports science, rehabilitation, and well-being. This work wants not only to contribute to the field of wearable technology but also to open up possibilities for further innovation in affordable, high-accuracy personal navigation and activity monitoring devices.

Acquiring and tracking multiple frequencies across many GNSS satellites introduces computation complexity, resulting in higher power consumption. In traditional receivers, various methods are employed to select and/or reject measurements from a number of satellites and signals obtained after the tracking and data demodulation stage in the GNSS receiver. However, having a significantly large number of tracking channels does not guarantee good PNT performance coming at the cost of computation complexity and power consumption. The approach described in this paper is to develop an optimised signal selection algorithm that works in partnership with acquisition and re-acquisition, and still maintains a good PNT performance.

A relatively small but optimal list of signals and satellites may result in better performance without adding additional computation complexity load and unnecessary power loss. The objective of the proposed work is to generate an optimal list of satellites and signals and prioritise them for the receiver to track, minimising unnecessary processing. To achieve this, an optimal geometry is generated using a bootstrapping algorithm, which also introduces an innovative index to determine what to add to the satellites/signals list for prioritisation within acquisition and tracking.

To verify and validate the proposed algorithm, in an experimental data campaign, GNSS signals were collected at baseband in a dense, complex and highly dynamic multipath environment. The selected area for the experimental campaign was strategically planned because it has short-range tunnels, multiple flyovers, and high-rise buildings causing signal blockage, leading to the degradation of the navigation solution and even complete loss of lock. The data was processed using the innovative approach, the results of which were the prompt re-acquisition of services and notable reduction in re-acquisition time. Using our approach, we managed to achieve good availability of resilient PNT services with a minimum number of tracking channels being used.