Future smart cities will consist of a heterogeneous environment, including UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles), used for different applications such as last mile delivery. Considering the vulnerabilities of GNSS (Global Navigation System Satellite) in urban environments, a resilient PNT (Position, Navigation, Timing) solution is needed. A key research question within the PNT community is the capability to deliver a robust and resilient time solution to multiple devices simultaneously. The paper is proposing an innovative time dissemination framework, based on Iquia’s SDN (Software Defined Network) and quantum random key encryption from Quantum Dice to multiple users. The time signal is disseminated using a wireless IEEE 802.11ax, through a wireless AP (Access point) which is received by each user, where a KF (Kalman Filter) is used to enhance the timing resilience of each client into the framework. Each user is equipped with a Jetson Nano board as CC (Companion Computer), a GNSS receiver, an IEEE 802.11ax wireless card, an embedded RTC (Real Time clock) system, and a Pixhawk 2.1 as FCU (Flight Control Unit). The paper is presenting the performance of the fusion framework using the MUEAVI (Multi-user Environment for Autonomous Vehicle Innovation) Cranfield’s University facility. Results showed that an alternative timing source can securely be delivered fulfilling last mile delivery requirements for aerial platforms achieving sub millisecond offset.

In lock-step with the recent proliferation of Unmanned Aerial Vehicles (UAV) for a multitude of purposes, including commercial, research, military or recreational, the demand for safe and resilient systems has also risen. Heavier, more autonomous systems operating over increasingly critical environments such as cities have high requirements for risk mitigation that the default navigation solution of Global Navigation Satellite Systems (GNSS) aiding of Inertial Measurement Units (IMU) cannot easily satisfy, thus stimulating demand for research and development in those fields.

Multiple approaches to addressing this problem exist, all with their respective advantages and disadvantages. One such approach that recently gained some traction is to exploit so-called Signals of Opportunity (SoP) for navigation purposes. These are radio signals that are outside of user control and used passively, including but not limited to radio or TV networks (DAB+, DVB-T), satellite signals (Starlink, Iridium), or cellular networks [1].

This work expands on previous research by the Authors, which measured the time-domain stability of real-world cellular signals [2], and investigates their suitability for navigation purposes. To achieve this, simulated signals with similar characteristics to those observed during the previous study are fed into a navigation platform. Both range and velocity constraints can be considered. The results show an improvement over non-aided navigation, and provide indications under which circumstances such a system can be used. In using the same simulation setup as previous work [3], a direct comparison between cellular and angle of arrival-based aiding is possible.

[1] Kapoor, R. et al.: UAV Navigation using Signals of Opportunity in Urban Environments: An Overview of Existing Methods. https://doi.org/10.1016/j.egypro.2017.03.156
[2] Winter, A. et al.: Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover. https://doi.org/10.3390/s24010213
[3] Winter, A. et al.: Low-cost angle of arrival-based auxiliary navigation system for UAV using signals of opportunity. https://doi.org/10.1109/PLANS53410.2023.10139950.

Multipath is one of the most challenging factors to model and/or characterize in the GNSS observation error budget. For the case of ground stations, code phase static multipath is typically the largest contribution of local observation errors. Current approaches for multipath characterization include the analysis of code-minus-carrier (CMC) observables and the exploitation of multipath repeatability. This contribution presents an alternative strategy for multipath detection and characterization based on unsupervised and self-supervised machine learning methods. The proposed strategy makes use of observations in the Receiver Independent Exchange Format (RINEX), typically generated by GNSS receivers in ground stations, for model training and testing, without requiring the availability of labelled data. To assess the performance of the proposed strategy (data-based), a comparison with a model-based methodology for multipath error prediction using a digital twin model is carried out. Results from a test case using data from a monitoring station of the International GNSS Service (IGS) show a consistency between the two approaches. The proposed methodology is applicable for a similar characterization in any GNSS ground station.

In this paper, we introduce a robust beamforming technique using array antennas. The proposed solution constitutes two stages, the first stage exploits the space-alternating generalized expectation-maximization (SAGE) algorithm to decompose the received GNSS signal into its constituent signals, i.e., the direct and the reflected signals. The SAGE algorithm estimates, for the direct and reflected signals, the Angle of Arrival (AoA) and the received covariance matrix. The second stage, on the other hand, utilizes the Minimum Variance Distortionless Response (MVDR) algorithm to produce the weight vector that steers the main beam towards the satellite’s direction and the nulls towards the multipath effect. The MVDR uses the AoA of the direct path and the covariance matrix of the reflected path in order to minimize the multipath effect only. The experimental results reveals that the proposed technique outperforms the traditional MVDR beamforming technique in a complex canyon-like environment.

In this connected world, communication in all kinds of complex environments is crucial. As a result, indoor satellite communication could enable many new applications and use cases. In this study, we explore the potential of Low Earth Orbit (LEO) satellites to provide indoor coverage. This is done by evaluating the signal strength of Orbcomm LEO satellite signals in multiple indoor environments within a suburban home. Starting from IQ samples, we developed an algorithm to calculate the Carrier-to-Noise Density Ratio (C/N0) as a key performance metric to compare environments when the Carrier-To-Noise Ratio (CNR) is above 0 dB. By utilizing a Software Defined Radio (SDR) in combination with this algorithm, we were able to evaluate the signal strength differences between environments. We found that the LEO satellite signals penetrated into every environment including the basement. The signals were even received with high signal strength in the attic, reaching values above 55 dB-Hz. Moreover, the signals were well received in every above-ground environment. Unsurprisingly, the satellite signals were received the weakest in the basement and only for a short duration of
time.

Launcher’s navigation systems have traditionally relied on high-grade INS (Inertial Navigation Systems) to inject a payload in the desired orbit. As launcher operations become more frequent and complex, even involving the automatic landing of re-entry stages, GNSS (Global Navigation Satellite System) positions itself as an enabler technology to guarantee the success of the operations, not without its challenges to solve.

The main objective of this work is the design of a hybrid GNSS/INS navigation system and FDIR (Fault Detection Identification and Recovery) algorithms used to demonstrate the robustness against errors in GNSS and INS technology. The navigation solution is provided by a modular sensor fusion algorithm architecture, which combines inertial, GNSS, radar-altimeter and star sensor measurements to satisfy the accuracy requirements for all the flight phases. Indeed, the architecture reflects the need to adapt to multiple launcher configurations such as expendable launch vehicle (Vega, Ariane-5), micro launchers (Shefex-2), reusable first stage boosters (Falcon-9) and unmanned re-entry vehicles (Space Rider), in which the most critical phases of the flight have been considered for the study.

The performance of the navigation system is assessed both in ideal conditions and under meaningful threats/failures which aid in the development of the FDIR algorithms. The threats include GNSS signal outages/loss of tracking, satellite/receiver clock bias/drift discontinuities, spoofing, receiver hardware failures, IMU saturation, vibration rectification, coning & sculling effect, and INS software numerical failures. To that purpose, we developed a simulation environment consisting of a RFCS (Radio Frequency Constellation Simulator), a Qascom QN400 space receiver and MATLAB software, which in turn enables the simulation of the navigation sensors, the desired threats/failures, and the validation of the navigation system performance. The simulation environment also allows the selection of different inertial sensor models or the usage of a real GNSS receiver.

Jamming of Global Navigation Satellite Systems (GNSS) is now a major thread for its users. A jammed receiver will lose its fix at a certain distance and won’t be able to provide position, navigation and timing (PNT) information. At larger distances there will be a fix to provide this PNT information, however the information will be less accurate as the carrier to noise (C/N) ratios of the received signals will be suppressed by the jammer. In this paper the (pseudorange) accuracy of a GNSS receiver under jamming is investigated in order to provide more insight into the effects of a jammer on the accuracy of a GNSS receiver. The theory as found in literature will be reviewed after which this theory will be evaluated by comparing theoretical results with actual measurements using a high-end GNSS signal simulator and a GNSS receiver.

The impact of a jammer on the delay lock loop (DLL) accuracy is primarily depending on the type of jammer signal, C/N and modulation of the received signal, and the DLL architecture (correlator spacing, bandwidth, etc.). As the C/N is the primary parameter that is influenced by the jammer, the focus lies on the relation between the jammer power and the C/N. Two different signal modulations are chosen for the analysis, first binary phase shift keying (BPSK) as used by GPS C/A, and second the (Composite) Binary Offset Carrier ((C)BOC) as used by Galileo E1. In theory the BOC code should be less affected by a jammer. In order to have full knowledge and control over the DLL architecture an open source software defined receiver, in this case GNSS-SDR, is used.

Requirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating demands on the heavy reliance on Global Navigation Satellite System (GNSS) services for Position, Navigation and Timing (PNT) solutions. This paper presents the integration of forecasted GNSS services into UAV path planning to meet such criteria.
UAVs often operate under stringent environments with rigid resilience requirements, and face challenges caused by dense, dynamic, complicated, and uncertain obstructions. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath), and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure flight safety.
Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a Reinforcement Learning (RL) approach to feature GNSS quality awareness during path planning. The non-linearity relations between GNSS quality in the form of Dilution of Precision (DOP), geographic locations, and the policy of searching sub-minima points are learned by clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed for maximising the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA in the Godot engine, incorporating forecasted DOP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36\% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV's DOP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity.

As we approach the peak of solar cycle 25, we observe increasing ionospheric and scintillation activity, which is negatively impacting the quality of GNSS measurements and presenting challenges in the positioning domain. Ionospheric refraction and diffraction introduce delays and distortions to the GNSS carrier phase measurements, leading to positioning errors that exceed the anticipated accuracies. These position errors can be a significant concern for users across the world who depend on precise GNSS positioning such as agriculture, offshore marine positioning, autonomous automotive positioning. To understand the direct impact on NovAtel receivers and its positioning engines, a comprehensive analysis was conducted. We take a closer look at what happened in 2023-2024 by characterizing the scintillation using the amplitude scintillation index (S4) values in an equatorial region. Additionally, the scintillation effect on the receivers through the analysis of CN₀, lock-breaks, double differences and other indicators were characterized. With a substantial amount of data collected at 20° latitude, where high solar activity occurs due to the proximity to the equator, the positioning performance of RTK and PPP were analyzed. As an example, Figure 1 below displays 24 hours of data collected in February 2023, the scintillation period is easily noticeable from approximatively 57000 to 597000 seconds GPS where we observe CN₀ variation of up to 18 dB-Hz over a period of 1 second while variations are less than 1 dB-Hz outside of the scintillation period. Figure 2 shows the spatial ionospheric decorrelation for a 25km baseline using a double difference between a base and a rover. Errors of over 6 meters are observed during the scintillation period. Figure 3 shows the PPP results compared to RTK during the scintillation period. While the PPP solution error did increase during the scintillation period, it remained below 10cm and easily outperformed the RTK solution. Figure 4 shows 3D graphs of S4 values as a function of UTC time for a 30-day period (August 2023).

Figure 1: CN₀ for all GPS L1CA satellites

Figure 2: Double Difference ADR for all GPS L1CA satellites

Figure 3: RTK and PPP Position Error (Continuous)

Figure 4: S4 values for August 2023 during 24h UTC

The diffusion of smartphones providing multifrequency GNSS measurements, including carrier phase observations, has opened new opportunities for the achievement of high-accuracy position from low-cost devices. This paper showcases the effectiveness of using GNSS observations from the triple-frequency Huawei P40 Mate smartphone for Real Time Kinematic processing under different environments and dynamic conditions.

Since the quality of the GNSS measurements is affected by the low-quality smartphone antenna, we conducted a preliminary analysis to quantify the antenna impact on the measurements. Survey and patch antennas were connected to the smartphone under open sky/static conditions for comparison purposes. Several key performance indicators were analysed: the signal power, the tracked satellite number, the positioning accuracy, and the percentage of fix solutions using bases at different distances (i.e. 5, 10 and 25 km).

The GNSS measurement quality is enhanced using external antennas, and the signal power is increased by about 8 dB-Hz for GPS L1 and L5 exploiting a survey antenna. However, the paper shows that, even using the smartphone's antenna, about 99% of fix solutions and centimetre positioning accuracy can be achieved by ad hoc tuning the RTK algorithm.

Moreover, a dynamic test was conducted fixing the smartphone to the windshield of a tractor (Figure 1) and two automotive tests were performed in a rural environment with the smartphone fixed to the car's windshield and roof. The smartphone and the reference trajectories (in orange and in green) are reported for the test with the tractor (Figure 2) and for a section of the test with the smartphone inside the car (Figure 3). The RTK tuning was crucial for increasing the fix solutions allowing to the smartphone trajectories to closely follow the reference ones.

This paper will discuss the results obtained by the above tests and the tuning of the RTK algorithm for coping with smartphone measurements.