Principal Gaussian Overbound for Heavy-Tailed Error Bounding

Purpose The purpose of this paper is to present the problem of implementation of the differential global navigation satellite system (DGNSS) differential technique for aircraft accuracy positioning. The paper particularly focuses on identification and an analysis of the accuracy of aircraft positioning for the DGNSS measuring technique. Design/methodology/approach The investigation uses the DGNSS method of positioning, which is based on using the model of single code differences for global navigation satellite system (GNSS) observations. In the research experiment, the authors used single-frequency code observations in the global positioning system (GPS)/global navigation satellite system (GLONASS) system from the on-board receiver Topcon HiperPro and the reference station REF1 (reference station for the airport military EPDE in Deblin in south-eastern Poland). The geodetic Topcon HiperPro receiver was installed in Cessna 172 plane in the aviation test. The paper presents the new methodology in the DGNSS solution in air navigation. The aircraft position was estimated using a “weighted mean” scheme for differential global positioning system and differential global navigation satellite system solution, respectively. The final resultant position of aircraft was compared with precise real-time kinematic – on the fly solution. Findings In the investigations it was specified that the average accuracy of positioning the aircraft Cessna 172 in the geocentric coordinates XYZ equals approximately: +0.03 ÷ +0.33 m along the x-axis, −0.02 ÷ +0.14 m along the y-axis and approximately +0.02 ÷ −0.15 m along the z-axis. Moreover, the root mean square errors determining the measure of the accuracy of positioning of the Cessna 172 for the DGNSS differential technique in the geocentric coordinates XYZ, are below 1.2 m. Research limitations/implications In research, the data from GNSS onboard receiver and also GNSS reference receiver are needed. In addition, the pseudo-range corrections from the base stations were applied in the observation model of the DGNSS solution. Practical implications The presented research method can be used in a ground based augmentation system (GBAS) augmentation system, whereas the GBAS system is still not applied in Polish aviation. Social implications The paper is destined for people who work in the area of aviation and air transport. Originality/value The study presents the DGNSS differential technique as a precise method for recovery of aircraft position in civil aviation and this method can be also used in the positioning of aircraft based on GPS and GLONASS code observations.

For almost two decades mobile mapping systems have done their georeferencing using Global Navigation Satellite Systems (GNSS) to measure position and inertial sensors to measure orientation. In order to achieve cm level position accuracy, a technique referred to as post-processed carrier phase differential GNSS (DGNSS) is used. For this technique to be effective the maximum distance to a single Reference Station should be no more than 20 km, and when using a network of Reference Stations the distance to the nearest station should no more than about 70 km. This need to set up local Reference Stations limits productivity and increases costs, especially when mapping large areas or long linear features such as roads or pipelines. <br><br> An alternative technique to DGNSS for high-accuracy positioning from GNSS is the so-called Precise Point Positioning or PPP method. In this case instead of differencing the rover observables with the Reference Station observables to cancel out common errors, an advanced model for every aspect of the GNSS error chain is developed and parameterized to within an accuracy of a few cm. The Trimble Centerpoint RTX positioning solution combines the methodology of PPP with advanced ambiguity resolution technology to produce cm level accuracies without the need for local reference stations. It achieves this through a global deployment of highly redundant monitoring stations that are connected through the internet and are used to determine the precise satellite data with maximum accuracy, robustness, continuity and reliability, along with advance algorithms and receiver and antenna calibrations. <br><br> This paper presents a new post-processed realization of the Trimble Centerpoint RTX technology integrated into the Applanix POSPac MMS GNSS-Aided Inertial software for mobile mapping. Real-world results from over 100 airborne flights evaluated against a DGNSS network reference are presented which show that the post-processed Centerpoint RTX solution agrees with the DGNSS solution to better than 2.9 cm RMSE Horizontal and 5.5 cm RMSE Vertical. Such accuracies are sufficient to meet the requirements for a majority of airborne mapping applications.

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of −55dBm and a conversion gain varying from −2.5dB to −9dB depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications.

The Global Navigation Satellite Systems (GNSS) is widely used in numerous applications that include the agricultural production, aviation, maritime and automobile navigation. GNSS is also involved with the lifesaving application such as the search and rescue. The GNSS is continuously developed with the additional satellite constellations being added to the orbit and the new frequencies are adopted. The new frequencies and the new signal are introduced to eliminate the largest errors that were ever experienced in the GNSS applications and improve the accuracy in terms of the position calculations. The signal distortions are caused by the GNSS signal degradation and the signal multipath becomes the prominent concern. The purpose of this paper is to assess and quantify the impact of the nominal signal interference in the in the GNSS receiver and further provide the brief description and the algorithm of the signal from the space. The results will be open and available for domain use for the researchers or institutions that are studying toward the solution based approach of increasing the GNSS performances. Furthermore it will provide an option to quantify the distortion impact to the GNSS.

The GNSS NavAer project has been under development since 2017, focusing on creating a comprehensive infrastructure dedicated to monitoring and analyzing space weather phenomena. This project represents a significant investment in scientific research and technology, aimed at enhancing our understanding of various atmospheric and environmental processes. The infrastructure established through the GNSS NavAer project is versatile and can be deployed across several scientific disciplines, including Geodesy, Aeronomy, and Space Weather, among others.One of the primary motivations behind the GNSS NavAer project is the increasing reliance on Global Navigation Satellite Systems (GNSS) for navigation, communication, and positioning applications. As these systems become more integral to modern society, understanding the factors that can disrupt their performance is of great insterest. Space weather events, particularly those affecting the ionosphere, have been identified as significant sources of interference that can degrade the accuracy and reliability of GNSS signals.Ionospheric scintillation, a phenomenon characterized by rapid amplitude and phase fluctuations of GNSS signals, poses a particular challenge. This can result from natural irregularities in the ionosphere, often intensified by solar activity. When these irregularities are present, GNSS signals can experience disruptions that may lead to positioning errors, loss of signal lock, and degraded navigation accuracy. Such disruptions can have serious implications for air navigation, autonomous systems, and any application that relies on precise positioning data.In this presentation, we will focus on the specific use of the GNSS NavAer infrastructure for monitoring space weather, with an emphasis on IS. The infrastructure includes a network of strategically placed GNSS receivers that continuously collect real-time data on ionospheric conditions, which are stored in a databank and can be explored via a dedicated toll specially developed for that (ISMR Query Tool). By integrating advanced data processing techniques and algorithms, researchers can analyze these data to identify and characterize scintillation events as they occur.The significance of this monitoring cannot be overstated. By providing timely information on ionospheric conditions, stakeholders can better prepare for and mitigate the impacts of space weather on GNSS applications. For instance, real-time observations can help aviation authorities make informed decisions regarding flight operations, ensuring that aircraft can navigate safely even in conditions that would typically be challenging due to ionospheric disturbances.Furthermore, the GNSS NavAer project facilitates collaborative efforts among scientists and researchers in various fields. By sharing data and findings, the project promotes a more comprehensive understanding of space weather and its impacts. International cooperation is vital, as space weather events often affect multiple regions simultaneously, making a collaborative approach essential for effective monitoring and response strategies.In conclusion, the GNSS NavAer project is a groundbreaking initiative aimed at enhancing our ability to monitor and understand the complexities of space weather. Through its dedicated infrastructure, it addresses the challenges posed by IS and aims to provide solutions that improve the reliability of GNSS applications. The deterioration in positioning and navigation will be presented, together to some trial to reduce such problem.

Service robotics is becoming a reality in many aspects of daily life due to the successful merger of several enabling technologies from the fields of Information and Communication Technologies. The growing availability of mass market products is also driving innovation towards automation in several civil and scientific areas. Specific applications such as precision farming, surveying, rescue operations and remote exploration can benefit from such modern and affordable robots, thus helping humans in work-related or daily life. To support such a new generation of service robots and vehicles, navigation capabilities play a fundamental role in mission planning and on-field activities. In parallel, recent developments of ultra-low cost embedded Global Navigation Satellite System (GNSS), such as in Android™ smartphones has enabled affordable Location Based Services (LBS) applications and provided an impetus to research into low cost navigation solutions. Modern smartphones come with an advantage of having a ubiquitous network infrastructure with ease of developing interconnected applications and integrated proprioceptive and exteroceptive sensors. Within the framework of precision agriculture promoted by the Politecnico di Torino Interdepartmental Centre for Service Robotics, this work investigates a low-cost navigation strategy for an Unmanned Ground Vehicle (UGV) – Unmanned Aerial Vehicle (UAV) paradigm exploiting a collaborative ranging technique based on Differential GNSS (DGNSS) approach applied on raw measurements exchanged between Android™ smartphones.

This contribution introduces a Bayesian framework of fault detection, identification, and adaptation (Bayesian DIA) methods for global navigation satellite system (GNSS) applications. It provides an alternative to the classical DIA approach, which allows for leveraging the prior information about faults to enhance the robustness of DIA estimators and subsequently use posterior information to implement quality control. In this framework, the Bernoulli–Gaussian model is first used to construct the prior distribution of faults describing prior information about the mode and size of faults. Next, a DIA method based on Bayesian hypotheses testing is proposed to process the additive faults in linear observation systems. Finally, the Bayesian DIA probability and credibility levels are introduced as measures for quality control. These probability levels describe the probabilities of decisions conditioned on the realities, which enable the prediction of the possibility of making a correct decision. The credibility levels denote the probabilities of realities conditioned on the decisions, which is helpful for the assessment of decision correctness. GNSS examples verified that the proposed Bayesian DIA method is robust for detecting and identifying faults with different modes and sizes.

The Global Navigation Satellite Systems (GNSS) applications are growing and more pervasive in the modern society. The presence of multi-constellation GNSS receivers able to use signals coming from different systems like the american Global Positioning System (GPS), the european Galileo, the Chinese Beidou and the russian GLONASS, permits to have more accuracy in position solution. All the receivers provide always more reliable solution but it is important to monitor the possible presence of problems in the position computation. These problems could be caused by the presence of impairments given by unintentional sources like multipath generated by the environment or intentional sources like spoofing attacks. In this thesis we focus on design algorithms at signal processing level used to assist Integrity operations in terms of Fault Detection and Exclusion (FDE). These are standalone algorithms all implemented in a software receiver without using external information. The first step was the creation of a detector for correlation distortion due to the multipath with his limitations. Once the detection is performed a quality index for the signal is computed and a decision about the exclusion of a specific Satellite Vehicle (SV) is taken. The exclusion could be not feasible so an alternative approach could be the inflation of the variance of the error models used in the position computation. The quality signal can be even used for spoofinng applications and a novel mitigation technique is developed and presented. In addition, the mitigation of the multipath can be reached at pseudoranges level by using new method to compute the position solution. The main contributions of this thesis are: the development of a multipath, or more in general, impairments detector at signal processing level; the creation of an index to measure the quality of a signal based on the detector's output; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; An alternative method to compute the Position Velocity and Time (PVT) solution by using different well known algorithms in order to mitigate the effects of the multipath on the position domain.

Accurate orthometric height information is critical for a variety of urban geospatial applications, including cadastral mapping, infrastructure development, and flood risk assessment. Traditional techniques such as differential leveling, while precise, are labour-intensive, time-consuming, and often impractical in densely built or rapidly expanding urban environments. This study investigates the integration of Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs) and differential Global Navigation Satellite System (GNSS) observations for orthometric height determination in an urban environment. High-resolution DEMs were generated from UAV photogrammetry, while ellipsoidal heights obtained from GNSS surveys were converted to orthometric heights using the Earth Gravitational Model 2008 (EGM08). The resulting orthometric heights from both UAV and GNSS methods were compared against leveling-based reference heights to assess their accuracy and consistency. The UAV-derived orthometric heights achieved a Root Mean Square Error (RMSE) of 0.514 m, while the GNSS-derived orthometric heights yielded a slightly lower RMSE of 0.470 m. These results demonstrate the potential of integrating UAV photogrammetry with GNSS and geoid model for generating reliable and accurate orthometric heights, offering a cost-effective and efficient alternative to conventional surveying in urban areas where access may be constrained.

This paper proposes a novel framework for detecting and mitigating multipath interference in Global Navigation Satellite Systems (GNSS), a common issue caused by reflections of signal off surfaces such as buildings and the ground. To address this challenge, the study integrates Differential GNSS (DGNSS) techniques with advanced machine learning models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Random Forest—to automatically detect and exclude satellites affected by multipath. The methodology involves synchronized GNSS data collection from a stationary base station and a mobile rover using high-precision u-blox ZED-F9P receivers with polarized antennas, DGNSS corrections via single and double differencing, and feature vector construction from both corrected and raw observation data. Signal quality labels (“Clean” or “Noise”) are derived through skyplot analysis and environmental modeling. Three classification approaches are explored: direct classification using DGNSS-derived vectors (Approach 1), image-based classification (Approach 2), and classification using combined feature vectors (Approach 3). Experimental evaluation using 2,312 labeled samples shows that ensemble learning significantly outperforms single-model classifiers for multipath detection. Random Forest achieves the highest performance across all approaches, reaching up to 99.75% accuracy, while CNN outperforms traditional methods, reaching up to 86.77% accuracy in image-based classification in Approach 2. The findings demonstrate the effectiveness of the framework in identifying and excluding compromised satellite signals, which has the potential to enhance the accuracy of GNSS positioning, with potential for real-world application in complex urban environments.

Nowadays, also thanks to the modernization of the Global Navigation Satellite Systems (GNSS), the number of applications based on the user position is increasing very quickly. Since for many of those applications the accuracy level is one of the core requirements, the monitoring of the interference produced by other telecommunication systems has a fundamental role in the design of GNSS receivers. In fact, because of the useful signal reaches the receiver antenna characterized by a very low power level, the interference from other electromagnetic sources can become one of the main causes of the signal degradation. Moreover it is well known that the frequency spectrum appears very busy and the characteristics of the signals that can interfere with the GNSS one are very different from each other.¹ For this reason the algorithms able to detect or mitigate the effect of an interference should be precisely designed for each different interference source. The presented technique is suitable for non-stationary signals and it develops into two different steps: the detection of the presence of the interference and the characterization of it. During the second phase the technique is able to estimate the time and frequency characteristics of the undesired source. The algorithm is based on the analysis of the auto-correlation function of the received signal, which is composed by the GNSS signal, the noise and the interference, and it exploits the particular characteristics of the auto-correlation of the noise and of the GNSS signals. In the paper the performance of the algorithm are shown for its application to pulsed signals. Moreover the paper shows the benefits obtained by introducing an algorithm based on the modal analysis during the detection phase.

Performance evaluation of a real-time high-precision landslide displacement detection algorithm based on GNSS virtual reference station technology

Cooperative positioning (CP), which fuses the position-related information of a group of vehicles, is a promising technique to enhance the performance of Global Navigation Satellite Systems (GNSS)-based methods for vehicular positioning in urban environments. With the development of communication techniques, more vehicles can be accessed to build the CP cluster. However, current CP techniques are mainly concentrated on the positioning methods and lack analysis of cooperator selection, and existing node selection methods only consider the ranging information between nodes. In this paper, to build a multi-vehicle CP cluster considering GNSS information, the Cramer-Rao lower bound (CRLB) based on differential GNSS and Ultra-Wide Band (UWB) CP framework is derived, and we demonstrate that the satellite observations have an important impact on CP performance. In order to comprehensively consider the impact of GNSS and UWB on CP cluster, we extend the Greedy search and generalized Breiman, Friedman, Oishen, and Stone (GBFOS) algorithm to select cooperators. Simulation results show that a CP cluster with better performance can be built, compared to only considering the ground ranging.

An accurate cooperative positioning system for vehicular safety applications

Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of 6.61×10−6, slightly lower than the desired level of 10−5.