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This paper presents a prototype, on-water rowing monitoring system and its testing results for a single scull boat. The proposed system aims at recording critical kinetic (athlete biomechanics and oar/seat movements) and kinematic (boat position, velocity, acceleration, and attitude) parameters for sport performance evaluation and rowing technique improvement. The data acquisition unit is organized in two parts: the first part aims at logging boat kinematics based on GNSS/INS filtering, while the second one facilitates kinetics data recording using a series of analog sensors (potentiometers, strain gauges) installed on the athlete’s body and the boat seat and oars. Both parts are connected to a central unit featuring analog voltage digitizers and a micro-PC for device handling and data storing. In order to test the performance of the system a series of field trials were undertaken featuring different observation scenarios as well as intentionally induced errors in the rowing technique. Analysis revealed the high performance of the system in terms of sensor completeness and setup procedures as well as operational efficiency. Moreover, system performance evaluation exercised through studying raw data recordings and resultant parameters at stroke cycle and average (standardized) stroke cycle level confirmed the fruitfulness of the proposed approach and system and its potential for implementation on a broad scale. Finally, the data acquired from the proposed system were used to compute the adopted input parameters and performance indicators to characterize the system in terms of functionality and operational efficiency.

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.

Nowadays, Unmanned Aerial Vehicles represent a very popular tool used in dramatically wide range of applications: indeed, their high flexibility, ease of use, and in certain cases quite affordable price make them a very attractive solutions in a number of applications, including surveying and mapping. Despite such a wide range of uses, their usage in automatic/autonomous mode is still restricted by the requirement of the availability of a reliable positioning and navigation system, which in practically all the commercial solutions is represented by the Global Navigation Satellite System (GNSS). Unfortunately, the availability and reliability of GNSS cannot be ensured in all the working conditions of interest. In particular, such condition may not hold downtown, close to high buildings. Since this can also be an operative condition of wide interest, this paper aims at investigating the use of an alternative positioning method that can be integrated with GNSS in order to compensate its unavailability. To be more specific, this paper investigates the positioning performance of an Ultra Wide-Band (UWB) system when an UWB rover is attached to a drone flying close to a building facade, whereas a set of UWB anchors are on the ground, close to the facade. The results obtained in the case study of a building of the University of Padua show that the UWB system positioning performance is quite good (quite less than 1 meter error for most of the time) up to approximately 15–20 meters of distance from the anchors. Close to the top of the building the error significantly increases when using an Extended Kalman filter (EKF) positioning approach, probably mostly due to the low UWB measurement success rate at such heights and to the poor geometric configuration of the UWB network. Nevertheless, a Gauss-Newton-based positioning strategy outperforms the EKF in such critical case, still ensuring errors at 1 meter level.

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 availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.

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.

Nowadays, assessing geo-hazards in cultural heritage sites in most cases takes place after the hazard has occurred. Monitoring structural and ground deformation resulting from geo-hazards facilitates the early recognition of potential risks and encourages effective conservation planning. This paper presents an integrated ground deformation monitoring approach based on the combined use of satellite SAR data, campaign-based GPS/GNSS observations, and aerial images from UAVs within the Choirokoitia UNESCO World Heritage Site in Cyprus. The Neolithic settlement of Choirokoitia is one of the most important prehistoric sites in the Eastern Mediterranean. The site is located on a steep hill, which makes it vulnerable to rock falls and landslides. As part of the PROTHEGO project, a series of field measurements were collected at the Choirokoitia site and compared against satellite SAR data to verify kinematic behavior of the broader area and to assist in monitoring potential geo-hazards over time. The results obtained indicate displacement rates of the order of 0.03 m/year. These results indicate that ground deformation should be monitored in the area surrounding the Choirokoitia using long-term, low-impact monitoring systems such as SAR images and UAV-based and geodetic techniques. The combination of such monitoring technologies can be compared to monitor and assess potential geo-hazards on archeological sites with increased accuracy. This paper was developed under the auspices of the activities of the “ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment”- “EXCELSIOR” project that has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 857510 and from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development. From 1 October 2019, the ERC group (Department of Civil Engineering and Geomatics) at the Cyprus University of Technology is on the way to be upgraded to ERATOSTHENES Centre of Excellence (ECoE) through “EXCELSIOR” H 2020 Widespread Teaming project ( www.excelsior2020.eu ). The project “Cyprus Continuously Operating Natural Hazards Monitoring and Prevention System” (CyCLOPS) RPF/INFRASTRUCTURES/1216 /0050 is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation in the framework of the RESTART 2016-2020 Research Programme. The “PROTection of European Cultural HEritage from GeO-hazards (PROTHEGO)” project HERITAGE PLUS/0314/36 is funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) – HERITAGE PLUS under ERA-NET Plus and the Seventh Framework Programme (FP7) of the European Commission and the Cyprus Research Promotion Foundation, contract KOINA/ΠΚΠ-HERITAGE PLUS/0314/36.

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 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.