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Dynamic characterization and Structural Health Monitoring (SHM) are crucial tools, of increasing demand, for reliable operation and predictive maintenance of large infrastructures, as the percentage of critically ageing infrastructures is growing steadily. We present a minimally invasive and synchronous fiber optic monitoring system for SHM, based on Phase-Optical Time-Domain Reflectometry (Phase-OTDR), and we assess its applicability and performance on a modular Bailey-type bridge of 1 : 2.5 scale. Phase-OTDR systems, along with other fiberoptic-distributed techniques have proven their capabilities in long-range SHM applications, although their complexity and high cost limits drastically their applicability and SHM market penetration. Here, we propose the use of a prototype Phase-OTDR system, featuring customized interrogation instrumentation with a balanced trade-off between performance and cost. Its experimental validation is achieved by comparison with well-established commercial monitoring systems, such as Ground-Based Radar Interferometer (GBRI), laser tracker, and multipoint optical Fiber Bragg Gratings (FBGs), in various excitation conditions and structure-damage scenarios, easily implementable in the model bridge. Finite-element modelling (FEM) and simulations were employed to study the bridge behaviour and provide a reference and comparison framework for the experimental characterization. The Phase-OTDR system successfully detected the structural behaviour in an efficient distributed manner, demonstrating comparable performance to commercial point sensor systems, thus demonstrating its application potential.

Due to the COVID-19 pandemic, distance learning had to be increasingly implemented at universities, and more e-learning formats had to be applied. The LBS2ITS project carried out under the lead of the Department of Geodesy and Geoinformation at TU Wien (TUW), Austria, came at the right time for these tasks. Education in Location-Based Services (LBS) is put to a new level including interactive e-learning and Problem-Based Learning (PBL) pedagogy. In the courses modernization, special attention is paid to the development and/or update of the courses to be implemented with these two pedagogic forms. Thus, teaching with an emphasis on learning outcomes is a central theme in the LBS2ITS project. To achieve this goal, the active verbs used in updated Bloom’s taxonomy for teaching on learning outcomes, i.e., remembering, understanding, applying, analyzing, evaluating, and creating, are applied to achieve the six levels of thinking and the active nature of learning. LBS2ITS will build a fully immersive and integrated LBS teaching and learning experience with the LBS application of Intelligent Transportation Systems (ITS) in mind. The outcome will be an innovative digital learning environment supporting synthetic and real-world PBL learning experiences. In the course of the project, a workshop for introduction of these new developments was held. This paper provides an insight into the results and experiences from this workshop. As e-learning and PBL must be combined and integrated nowadays, the new term PBeL (Problem-Based e-Learning) is proposed and introduced in this paper. The development of this approach and background information on the theory and the LBS2ITS project are presented.

The release of Android Raw GNSS Measurements API in mid-2016 has enabled the reconstruction of pseudorange, carrier-phase, Doppler and carrier-to-noise density (CN0) observables in low-cost, smart devices. This technical development has opened the door to numerous applications that necessitate increased Position-Velocity-Time (PVT) quality metrics. However, despite the recent advances in the field, deficiencies inherent to smartphones GNSS antenna and chipset, still account for excessive biases in the raw GNSS observables and for severe multipath effects in the satellite signal. RAIM (Receiver Autonomous Integrity Monitoring) techniques is one way to monitor and increase the performance of the PVT solution of a GNSS receiver. It is an augmentation method to detect and eventually reject anomalous (blunders or outliers) measurements and compute protection levels that bound position errors to preset limits. The implementation of RAIM technique to smartphone GNSS data is very limited and confined only to static positioning scenarios. This study presents the results obtained from the testing of the classic RAIM technique in a series of kinematic positioning trials in urban environment using two cotemporary smartphone devices (i.e., Xiaomi Mi 8 and One Plus Nord2 5G). In order to reconstruct a real use case, the smartphones were mounted on the vehicle dashboard. The reference trajectory was obtained using a tactical grade GNSS/IMU system (NovAtel® PwrPak7, iMAR IMU-FSAS) fixed on the NTUA test vehicle roof-top sensor platform. At a pre-processing stage the raw GNSS data have undergone through a Hatch filer. This step allows to smooth iteratively the noisy code pseudorange measurements using the precise (but ambiguous) phase carrier measurements. Data analysis includes two processing strategies. We employ the PANG-NAV open-source software released by LabPANG (PArthenope Navigation Group Laboratory) to compute the SPP (Single Point Positioning) solution in two ways. We produce the standard and the RAIM-based SPP solution using different parameter setups. The paper presents quantitative results and comparisons between the two smartphone-based solutions and the reference trajectory, and reveal the potential (benefits and limitations) of using the RAIM technique for smartphone positioning.

Precise modeling of the ionospheric Total Electron Content (TEC) is critical for reliable and accurate GNSS applications. TEC is the integral of the location-dependent electron density along the signal path and is a crucial parameter that is often used to describe ionospheric variability, as it is strongly affected by solar activity. TEC is highly depended on local time (temporal variability), latitude, longitude (spatial variability), solar and geomagnetic conditions. The propagation of the signals from GNSS (Global Navigation Satellite System) satellites throughout the ionosphere is strongly influenced by temporal changes and ionospheric regular or irregular variations. Here, we propose a deep learning architecture for the prediction of the vertical total electron content (VTEC) of the ionosphere based on GNSS data. The data used in many deep learning tasks until recently where mostly represented in the Euclidean space. However, geodesy studies data that have an underlying structure that is non-Euclidean space. Geospatial data are large and complex, as in the case of GNSS networks data, and their non- Euclidean nature has imposed significant challenges on the existing machine learning algorithms. The task of VTEC prediction is challenging mainly due to the complex spatiotemporal dependencies and an inherent difficulty in temporal forecasting. Spatial-temporal graph neural networks (STGNNs) aim to learn hidden patterns from spatial-temporal graphs. The key idea of STGNNs is to consider spatial and temporal dependency at the same time. Spatial Dependency: Assuming a network of permanent stations of International GNSS Service (IGS), each station represents a node of the graph, and their Euclidean distance is used to formulate the set of edges of the graph. Thus, we achieve exchange between nodes and their neighbors. Temporal dependency: The graph operates in a dynamic environment. Thus, we leverage the recurrent neural networks (RNNs) to model the temporal dependency. As a result, time series of VTEC data can be predicted to future epochs. Solar and geomagnetic indices are formulated as node attributes and are also present temporal variability.Topics to be discussed in the study include the design of the graph neural network structure, the training methods exploiting steepest descent algorithms, data analysis, as well as preliminary testing results of the VTEC predictions as compared, with state-of-the-art graph architectures.

The increasing demand for reliable indoor navigation systems is leading the research community to investigate various approaches to obtain effective solutions usable with mobile devices. Among the recently proposed strategies, Ultra-Wide Band (UWB) positioning systems are worth to be mentioned because of their good performance in a wide range of operating conditions. However, such performance can be significantly degraded by large UWB range errors; mostly, due to non-line-of-sight (NLOS) measurements. This paper considers the integration of UWB with vision to support navigation and mapping applications. In particular, this work compares positioning results obtained with a simultaneous localization and mapping (SLAM) algorithm, exploiting a standard and a Time-of-Flight (ToF) camera, with those obtained with UWB, and then with the integration of UWB and vision. For the latter, a deep learning-based recognition approach was developed to detect UWB devices in camera frames. Such information is both introduced in the navigation algorithm and used to detect NLOS UWB measurements. The integration of this information allowed a 20% positioning error reduction in this case study.

Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

The articles in this special section focus on recent advancements on the use of the global navigation satellite system (GNSS)-based positioning for intelligent transport systems. The civil applications of geopositioning are undergoing exponential development. The latest market analysis for global navigation satellite systems (GNSSs) shows two major fields of application which share the majority of the market: intelligent transport systems (ITS), mainly in the road ITS domain, and location-based services, accessible on smartphones and tablets. The modernization of GPS and Russia’s GLONASS system and the development of Galileo and Bei- Dou are proceeding at a fast pace, introducing improved potential capabilities and higher performance levels for satellite-based positioning, and leading to new architectures for positioning and new strategies for positioning by means of other sensors. GNSSs are considered the superior system to provide accurate and global position, velocity, and time.

The volume of freight transport on roads has grown steadily over the years and is expected to increase significantly over the next decade. Truck driver coaching systems have proved a key element in boosting efficiency, profitability and safety of the haulage sector whilst contributing to infrastructure asset management objectives. Notwithstanding new, commercial driver coaching systems have shown up recently in the market, still they operate as "black boxes", whilst their functionality and automation level is very limited. Given the enormous numbers of truck fleets currently operating worldwide, the necessity for an adaptable driver coaching system, capable of providing real-time functionality is evident. This study reports the design characteristics and first results from PEGASUS research project aiming at developing a self-trained, unified, driver-coaching system based on geolocation, IoT (Internet of Things) and BI (Business Intelligence) techniques. The system by design aims at reducing operational costs for the truck fleet, improving road safety, as well as contributing to green transportation. In effect, the system has been designed to produce user-friendly, two-way advice to lorry drivers tailored to the road conditions (e.g., geometry, road markings, neighboring vehicles) and operational (e.g., weather, traffic) conditions in real time and in a dynamic manner.

Pedestrian behavior is influenced by various factors and is thus characterized by heterogeneity. The aim of this research is to explore the impact of various parameters, such as pedestrians’ characteristics, mobile phone use and walking pace on pedestrians’ behavior. Classification of pedestrian behavior contributes into understanding how different factors affect pedestrian behavior and allows a finer perception of pedestrian movement, as it helps us distinguish and interpret the way that pedestrians react to different situations. In this research a simple methodology for pedestrian behavior classification is proposed taking into account pedestrians’ heterogeneity. The methodology employs random forest algorithms in order to analyze and classify trajectory data, as well as to estimate the pedestrian behavior state in real time. The analysis utilizes real pedestrian trajectory data collected via an experiment conducted in the Campus of National Technical University of Athens under various conditions (normal and fast pace, distraction from mobile phone). Distributions of these data are explored and a clustering analysis follows, yielding satisfactory results.

In this paper, we address the challenge of robust indoor positioning using integrated UWB and Wi-Fi measurements. A key limitation of any fusion algorithm is whether the distribution that describes the random errors in the measurements has been correctly specified. Here, we describe the details of a set of practical experiments conducted on a purpose built calibration range, to evaluate the performance of commercial UWB sensors with Wi-Fi measurements as captured by an in-house smartphone application. In this paper, we present comparisons of ranges from the UWB sensors and the Wi-Fi built into the smartphone to true ranges obtained from a robotic total station. This approach is validated in both static and kinematic tests. The calibration range has been established as one component of an indoor laboratory to undertake a more diverse research agenda into robust indoor positioning systems. The experiments presented here have been conducted collaboratively under the joint FIG (WG5.5) and IAG (SC4.2) working groups on multi-sensor systems.

Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.

Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. As part of the joint IAG/FIG Working Groups 4.1.1 and 5.5 on Multi-sensor Systems, a benchmarking measurement campaign was conducted at The Ohio State University. Initial experiments have demonstrated that Cooperative Localization (CL) is extremely useful for positioning and navigation of platforms navigating in swarms or networks. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were equipped with combinations of GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), Raspberry Pi units, cameras, Light Detection and Ranging (LiDAR) and inertial sensors for CL. Pedestrians wore a specially designed helmet equipped with some of these sensors. An overview of the experimental configurations, test scenarios, characteristics and sensor specifications is given. It has been demonstrated that all involved sensor platforms in the different test scenarios have gained a significant increase in positioning accuracy by using ubiquitous user localization. For example, in the indoor environment, success rates of approximately 97% were obtained using Wi-Fi fingerprinting for correctly detecting the room-level location of the user. Using UWB, decimeter-level positioning accuracy is demonstrable achievable under certain conditions. The full sets of data is being made available to the wider research community through the WG on request.

Traffic simulation is a mature field, with several decades of development. While many mainstream aspects can be assumed to be at a level where most challenges have been overcome, there are still edge cases that are not adequately covered. For example, traffic simulation of mixed networks in conditions close to exceeding capacity or in emergency cases is still a challenge. This research aims at developing a risk-reduction framework for large-scale parking facilities and depots (for either passenger vehicles or commercial fleets) under constraints. An integrated methodological framework is therefore analyzed and then the emphasis is placed on the localization of vehicles under these conditions using a combination of state-of-the-art technologies. A case study is implemented to test the feasibility of the proposed technologies including smart-phone accelerometers, radio frequency identification technology, high-sensitivity GNSS receivers and Ultra-wideband technology. Then, an experiment was conducted in a large-scale parking facility, including vehicle positioning in indoor, outdoor and hybrid environments. This information is used as input for a simulation-based evacuation analysis of a large parking facility in Athens, Greece. The evaluation of four evacuation scenarios suggests that considerable risk reduction benefits can be obtained by suitable emergency response strategies.

Global navigation satellite systems (GNSS) has become a kind of positioning standard due to the high penetration rate of this technology on mass market intelligent transportation systems (ITS) applications. However, this positioning technique remains a real challenge for very demanding ITS services which require continuity and a specific accuracy of the data. This study reports on a practical and methodological approach for the evaluation of the GNSS and multi-sensors positioning and attitude of vehicles in real life conditions. Test scenarios have been set up with several positioning sensors mounted on a vehicle for the collection of raw data on different road sections. The measurement of a high quality reference trajectory with an accuracy of several centimetres allows to estimate position accuracy under different environmental conditions. The authors will show in detail the results and identify some typical situations (e.g. sub-urban environment) where the quality of positioning parameters is reduced to several metres instead of dm and may impact the level of ITS services, e.g. road user charging or safety applications. This study results from the methodology developed in the satellite positioning performance assessment for road applications COST Action on the performance assessment of positioning terminals for ITS.

Today, the provision of high accuracy/integrity, real-time positioning is a key requirement for many urban mobility applications, especially those supporting emerging road safety services. At the same time, the increasing volumes of lower quality, user-collected positioning information via personal devices opens the road to new mobility services. However, despite the fast development of information and communication technologies and the expansion in multisensory systems, the positioning problem still exhibits many challenges in the deep urban environment due to the harsh operational conditions and the increasing complexity of the mobility systems and services. This paper offers a review of the positioning problem, its challenges and perspectives in the light of emerging intelligent transportation systems and personal mobility applications. It examines the user requirements, the positioning parameters and relevant standardization issues. The main positioning technologies and techniques are reviewed while a focus is placed on cooperative positioning. Specific cooperative intelligent transportation systems (C-ITS) case studies are analyzed. The article concludes with a critical discussion on the major issues concerned with positioning for C-ITS.

A relatively low-cost system for indoor parking facilities management is proposed, which is a combined solution of RFID/WiFi and a MEMS IMU monitoring scheme. An RFID localisation module is proposed in the form of so-called virtual gates. To define such virtual gates, either RFID tags or readers are placed at known locations throughout the area of interest. In this study, a number of tags are fixed at known positions and a moving reader is carried by each participating vehicle. Based on this configuration set-up, the Cell of Origin CoO technique is applied, in which the system indicates the presence of the user carrying the reader in a cell around the tag location. To define a virtual gate, tags are installed along the parking lot corridors and at critical transit passages in the parking facility. The CoO technique is also proposed in the case of WiFi for location determination of vehicles in a multi-storey car park. In this study, WiFi is employed to monitor the passing vehicles and bridge the gap until a tag can detect a user’s reader again. Thus, a combined positioning solution of RFID and WiFi is achieved. As a complement to the proposed RFID/WiFi system, this study examines the potential and limitations of MEMS IMU sensors i.e. accelerometers, gyroscopes and barometers commonly found in modern smartphones. The paper concludes with a detailed discussion on the implications of alternative positioning techniques for indoor parking management.

Recent improvements in the field of wireless technologies has led to an increase of available tools for Location Based Services (LBS). Modern indoor LBS applications continually raise the bar of requirements for the employed positioning technology and Ultra-Wide Band (UWB) ranging has become relevant by providing adequate performance. UWB employs modulated impulse signals for achieving ranging accuracies in the order of cm offering increased capabilities compared to other Radio-Frequency ranging methods. In order for the UWB systems to function reliably in complex indoor environments, error mitigation techniques are employed based on ranging error modelling methods. In this study, a commercial UWB system (Time Domain®) is employed for the development of error calibration models based on data collected in an indoor office environment. Three calibration methods are examined both for static and kinematic test scenarios. An overall improvement of 32%-86% is demonstrated for the static UWB ranges data while a 74% improvement on kinematic data results in a mean trajectory accuracy of 9 cm.

Global navigation satellite systems have tremendous impact and potential in the development of intelligent transportation systems and mobility services and are expected to deliver significant benefits, including increased capacity, improved safety, and decreased pollution. However, there are situations in which there might not be direct location information about vehicles, for example, in tunnels and in indoor facilities such as parking garages and commercial vehicle depots. Various technologies can be used for vehicle localization in these cases, and other sensors that are currently available in most modern smartphones, such as accelerometers and gyroscopes, can be used to obtain information directly about the driving patterns of individual drivers. The objective of this research is to present a framework for vehicle localization and modeling of driving behavior in indoor facilities or, more generally, facilities in which global navigation satellite system information is not available. Localization technologies and needs are surveyed and the adopted methodology is described. The case studies, which use data from multiple types of sensors (including accelerometers and gyroscopes from two smartphone platforms as well as two reference platforms), provide evidence that the opportunistic smart-phone sensors can be useful in identifying obstacles (e.g., speed humps) and maneuvers (e.g., U-turns and sharp turns). These data, when cross-referenced with a digital map of the facility, can be useful in positioning the vehicles in indoor environments. At a more macroscopic level, a methodology is presented and applied to determine the optimal number of clusters for the drivers’ behavior with a mix of suitable indexes.

For Wi-Fi positioning usually location fingerprinting or (tri)lateration are employed whereby the received signal strengths (RSSs) of the surrounding Wi-Fi Access Points (APs) are scanned on the mobile devices and used to perform user localization. Within the scope of this study, localization of an individual user or a group of users within a neighborhood is achieved using an integrated solution with Wi-Fi positioning and inertial navigation (IN). Differential approaches termed DWi-Fi are developed either in analogy to differential GNSS or Very Long Baseline Interferometry (VLBI). They make use of a network of reference stations (RSs) deployed in the area of interest on which continuous RSS observations are performed leading to correction parameters for the users in real-time which consider temporal and spatial variations of the Wi-Fi RSSs. Then path loss model errors for the RSS to range conversion are eliminated by differencing model–derived ranges from the same AP. Similar as in a CORS GNSS network a network solution is proposed where Flachenkorrekturparameter (FKPs) are derived using areal modelling. Furthermore, the detection of selected waypoints along a trajectory is performed for the integrated navigation solution. These waypoints serve as a position estimation update for the IN and are therefore referred to as intelligent checkpoints (iCPs). Also, this study introduces the concept of using Ultra-wide Band (UWB) technology as a means of supporting the Wi-Fi solution through dedicated checkpoints of higher positioning accuracy given its high accuracy potential and the continuously decreasing system cost. A number of tests have been undertaken to examine the validity and the potential of this approach and results of the analyses are presented. It could be proven that all major user requirements (i.e., positioning accuracy, availability, continuity) are being met. The full experimental schemes, practical results and algorithms developed are detailed in this paper.

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to 95 % . The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included.

In recent years, in addition to the GPS constellation, the ability to utilise extra satellites available in the GLONASS system enhanced the capabilities and possible applications of precise point positioning (PPP) method. However, the intrinsic differences between GPS and GLONASS and the lack of a fully tested global tracking network of multi-GNSS systems necessitate the evaluation of their combined use. This paper offers a comprehensive statistical assessment of static PPP using GPS-only, GLONASS-only and GPS/GLONASS combined data. Data analysis is performed in a short (hourly to daily) and a long (several weeks) term, spanning observations from eleven IGS stations. Evaluation of the results reveals insignificant differences in the accuracy and repeatability among the three satellite constellation solutions for long (8 h, 24 h) observation times. The superiority of the combined GPS/GLONASS solution is apparent for short (1 h, 2 h) observation periods, whereas analysis confirms the stability of PP...

This paper describes a new algorithm to aid stand-alone GNSS positioning in areas of bad signal reception using a Digital Elevation Model (DEM). Traditional Height-Aiding (HA) algorithms assume either a preset (fixed) value for the receiver elevation or rely on the elevation value that corresponds to the nearest available position fix. This may lead in erroneous receiver elevation estimates that, under circumstances, are inefficient to aid effectively GNSS positioning. In this study, the receiver elevation is updated at every iteration step of the navigation solution through dynamic interpolation of the elevation model. The algorithm, because of its ability to extract and fully exploit the elevation information derived from a digital model, it can prove particularly useful in forested areas with steep-sloped terrain. Extended test runs were undertaken to validate the correctness of the mathematical model and the feasibility of the algorithm and associated software. Particularly, analysis of a dataset acquired in a forested, rapidly undulating environment reveals significant average improvement in all performance metrics of positioning, namely the GNSS position availability (50%), accuracy (56%) and external reliability (86%) compared to the Standard Point Positioning (SPP) solution. Moreover, it was found that the method can cope successfully in marginal operating conditions with situations of bad satellite geometry and satellite signals affected by interference due to tree canopy. © 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

This paper presents a series of tests and analyses undertaken to investigate the performance capabilities of an active RFID system (Freaquent®) for asset tracking and personal mobility. The RFID system was tested exhaustively considering a number of user requirements including maximum operating distance, antenna orientation, position accuracy, the influences caused by nearby objects and the operating environment. For this purpose, various positioning techniques were applied based on the lateration and fingerprinting positioning concepts. Analysis revealed that the lateration technique is sensitive to operational environment; however, as long as a representative “RSS to distance” model is established the method can deliver accuracies of the order of 0.7 m at unobstructed line-of-sight conditions. Location fingerprinting has shown that large temporal variations can occur partly which effect the radio maps of RSS distributions significantly. Thus, RSS training measurements have to performed at regular intervals to guarantee acceptable positioning accuracies on the meter-level.

This paper presents the dynamic testing of a roadway, single-span, cable-stayed bridge for a sequence of static load and ambient vibration monitoring scenarios. Deck movements were captured along both sideways of the bridge using a Digital Image Correlation (DIC) and a Ground-based Microwave Interfererometer (GBMI) system. Cable vibrations were measured at a single point location on each of the six cables using the GBMI technique.Dynamic testing involves three types of analyses; firstly, vibration analysis and modal parameter estimation (i. e., natural frequencies and modal shapes) of the deck using the combined DIC and GBMI measurements. Secondly, dynamic testing of the cables is performed through vibration analysis and experimental computation of their tension forces. Thirdly, the mechanism of cable-deck dynamic interaction is studied through their Power Spectra Density (PSD) and the Short Time Fourier Transform (STFT) analyses. Thereby, the global (deck and cable) and local (either deck or cable) bridge modes are identified, serving a concrete benchmark of the current state of the bridge for studying the evolution of its structural performance in the future. The level of synergy and complementarity between the GBMI and DIC techniques for bridge monitoring is also examined and assessed.

This paper describes a novel approach for extracting the centerline geometry of road/railway alignments in the form of traditional design elements (i.e., straight lines, circle arcs, and clothoids). As opposed to previous research, the proposed method attempts a completely general and a fully automated solution to the problem in a rigorous mathematical manner. Centerline locations originate in a ground-based mobile mapping system (e.g., global navigation satellite system/inertial navigation system vehicle trajectory or kinematic laser scanning profiles of the road/railway corridor). The core of the algorithm resides on the use, manipulation, and suitable reformulations of the bearing diagram of the centerline locations and its first- and second-order derivatives. To ensure highly accurate and consistent results, the algorithm practices a series of specifically designed/dynamically tuned filters that fully adhere to the fractal properties of the centerline location data. Extended test runs were undertaken to validate the correctness of the mathematical model and the feasibility of the algorithms and associated software. In this paper, test results using a simulated and a real (based on a multisensor geodetic survey) subset of a railway track data are discussed.

The aim of the present work is to assess and to demonstrate the benefits of adopting optimal experimental design theory and techniques in order to enhance the potential of field data recorded using conventional geodetic instruments. More specifically, this research focuses on Robotic Total Stations (RTS) and in kinematic applications of geodetic positioning that exhibit a cyclic/periodic pattern of motion. The computational approach adopted follows from the principles of c- and D-optimal design criteria. Data processing involves computing the amplitude of motion in two ways; (a) using a sample of consecutively ordered data recordings and (b) using a sample respecting the optimal design criteria. Analysis, confirms the utility of the method resulting an improvement (i.e. a reduction) of the oscillation amplitude variance. This conclusion applies particularly at higher frequencies of oscillations (>1 Hz). This is important as at higher frequencies the performance of RTS deteriorates, and hence large variances occur.

This paper studies the long-term (>30 years) settlement behavior of the Mornos dam on the basis of finite element analysis and vertical displacement data. It compares actually measured deformations resulting from a continuous geodetic monitoring record of the dam behavior with a numerical back analysis. Our aim is to explain the actual deformation history on the basis of the mechanical behavior of the dam. The deformation monitoring record consists of precise leveling data of a large number of control stations established along the crest and the inspection gallery of the dam, as well as settlements derived by magnetic extensometers placed inside the dam body. Overall, the available data cover the phases of construction, first filling of the reservoir and most of the operational time of the dam. The numerical modeling assumes 2D plane-strain conditions to obtain the displacement and the stress–strain characteristics of the abutments and the dam at eleven equally-spaced cross sections. Comparative evaluation of the results of the geodetic monitoring analysis against the findings from the finite element back analysis simulating characteristic stages in the lifetime of the structure, shows a very good agreement (on average 0.03 m) between the measured and computed displacements, which testifies to the correctness of the geotechnical parameters and loads used in the analysis.

In this paper, a multi-sensor data acquisition system and a method for extracting the track axis geometry of railways are introduced. The rail track surveying system employs GPS and a tracking total station for absolute referencing, aided by a biaxial inclinometer for reducing the data into a common datum in canted sections and a digital odometer for linear referencing. Track axis extraction depends on rail track positional data, whereas its geometric modelling is performed on the basis of design elements using the bearing diagram and the Generalised Least Squares (GLS) method on the horizontal and vertical track layout. In addition, a supplementary algorithm for computing the rail curvature is presented. Extended test runs were conducted to validate the feasibility of the system and the method. The test results show that a directional uncertainty of better than 0.01° in tangent sections and deviations between the actual and computed track radii of better than 1% in curved sections are achievable.

Track geometry quality is a critical factor for the safety of modern railway systems. It is achieved through a number of track surveying parameters that, in essence, characterize the quality of the absolute and relative rail locations. Nowadays, two methods are widely used for acquiring accurate track surveying data; stand-alone geodetic surveys and kinematic track measuring systems. Despite the rapid growth in the use of track recording trolleys in the past few years, it has become clear that, depending on the special features of a construction project, the combined use of both systems can increase the reliability of track geometry quality with profound effects on construction costs and operational safety. This paper presents the basic concept of track geometry surveying, describes the surveying techniques and compares the rail laying quality measures obtained from the implementation of the two methods during the construction of the modern tramway of Athens.

Traffic simulation models have seen increasing use during the past decades. One of the biggest challenges related to their successful application is effective calibration and validation. Emerging data collection techniques provide richer data that can be used to improve this process. In this research, the authors explore the use of distributions of collected data (such as accelerations, using opportunistic sensors, such as smart-phone accelerometers) for calibration purposes. The performance of the considered ubiquitous sensors is benchmarked against reference equipment, to evaluate its accuracy under different conditions. A methodology is proposed for the integration of distributions of data in traffic simulation model calibration and validation.

Real‐time integrated monitoring of reliability (the impact of undetected biases in positioning observations on the position of geophysical elements) during marine seismic surveys is essential to ensure there is no significant loss in the resolution of the processed seismic image. The B-method is currently the most rigorous testing procedure for bias identification. It has been used extensively in geodetic networks, but to date it has found very little application within a dynamic environment. Here the method is expanded for use in dynamic systems—especially in marine seismic positioning networks such as those represented by a multisource, multistreamer, multivessel acquisition system. With this approach, we have found completely rigorous reliability estimates based on an integrated navigation method. We conclude that although the reliability of a system is in general a consequence of the redundancy, its geometry, and the stochastic model of the observations, these factors affect internal and external reliability in rather different ways. A key contribution is the demonstration that simple, easily understandable, and explainable statistics can be used to give a more complete description of quality than that obtained from precision measures alone. Assessing precision alone may give a false sense of security regarding the quality of the location of a seismic spread. The exploration and production industry is now making increasing use of old and new marine streamer 3-D surveys as part of 4-D time lapse reservoir monitoring projects. A more rigorous approach to positioning data quality, such as the method described in this paper, may play a material role in the avoidance of interpretable artifacts in these projects.

2013 10th Workshop on Positioning, Navigation and Communication, WPNC 2013 - Proceedings 2013, Article number 6533262 Collaborative (or cooperative) positioning or navigation uses multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAV's, team of robots, emergency crews and first responders. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation in a week at the University of Nottingham in May 2012. Different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans and personal navigators. The presented preliminary results of the field experiments show that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms. © 2013 IEEE.

With the rapid growth in smartphone technologies and improvement in their navigation sensors, an increasing amount of location information is now available, opening the road to the provision of new Intelligent Transportation System (ITS) services. Current smartphone devices embody miniaturized Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and other sensors capable of providing user position, velocity and attitude. However, it is hard to characterize their actual positioning and navigation performance capabilities due to the disparate sensor and software technologies adopted among manufacturers and the high influence of environmental conditions, and therefore, a unified certification process is missing. This paper presents the analysis results obtained from the assessment of two modern smartphones regarding their positioning accuracy (i.e., precision and trueness) capabilities (i.e., potential and limitations) based on a practical but rigorous methodological approach. Our investigation relies on the results of several vehicle tracking (i.e., cruising and maneuvering) tests realized through comparing smartphone obtained trajectories and kinematic parameters to those derived using a high-end GNSS/IMU system and advanced filtering techniques. Performance testing is undertaken for the HTC One S (Android) and iPhone 5s (iOS). Our findings indicate that the deviation of the smartphone locations from ground truth (trueness) deteriorates by a factor of two in obscured environments compared to those derived in open sky conditions. Moreover, it appears that iPhone 5s produces relatively smaller and less dispersed error values compared to those computed for HTC One S. Also, the navigation solution of the HTC One S appears to adapt faster to changes in environmental conditions, suggesting a somewhat different data filtering approach for the iPhone 5s. Testing the accuracy of the accelerometer and gyroscope sensors for a number of maneuvering (speeding, turning, etc.,) events reveals high consistency between smartphones, whereas the small deviations from ground truth verify their high potential even for critical ITS safety applications.

2012 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2012 - Conference Proceedings 2012, Article number 6418893 An integrated positioning solution termed 'collaborative positioning' employs multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAV's, team of robots, emergency crews and first responders. The stakeholders of the solution (i.e., mobile sensors, users, fixed stations and external databases) are involved in an iterative algorithm to estimate or improve the accuracy of each node's position based on statistical models. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation, and partially indoor navigation. For this purpose different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans, a personal navigator and a foot tracker unit. In terms of the tests, the data from the different platforms are recorded simultaneously. Several field experiments conducted in a week at the University of Nottingham are described and investigated in the paper. The personal navigator and a foot tracker unit moved on the building roof, then trough the building down to where it logged data simultaneously with the vans, all of them moving together and relative to each other. The platforms then logged data simultaneously covering various accelerations, dynamics, etc. over longer trajectories. Promising preliminary results of the field experiments showed that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms. © 2012 IEEE.

This paper presents the mathematical models developed for the design of a pedestrian, cable stayed bridge, the geodetic monitoring procedures and their detailed cross-comparisons and analyses undertaken during the phases of construction and at commissioning stage. Because of the asymmetric design, the relative flexibility of the pylons, the eccentric positioning of the central joint and the off-plane low angles of the cables, a multi-sensor, event based geodetic monitoring scheme was adopted to assess the structural integrity of the bridge. During construction, the bridge kinematics were measured along both sides of the deck and at selected points on the pylon facades using a digital level and a high accuracy total station used in metrology respectively. At a commissioning stage, a number of load test series were undertaken using conventional geodetic techniques and a tactical grade Inertial Measurement Unit (IMU). Analysis confirmed the overall mathematical modelling assumptions made for the individual s...

Measures of both precision and reliability are nowadays used to assess the quality of positioning a seismic spread offshore. Precision is related to positioning uncertainties due to random measurement errors of source and receiver groups and is measured by means of standard deviations and error ellipses. However, reliability is a measure of the ease with which biases (gross measurement errors) may be detected (internal) and the impact of undetected biases on the source and receiver position (external). This paper describes a method for the rigorous assessment of reliability within an integrated navigation approach. The method is tested with real multi-streamer positioning data.