A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS

When compared to the wide range of atmospheric sensing techniques, global navigation satellite system (GNSS) offers the advantage of operating under all weather conditions, is continuous, with high temporal and spatial resolution and high accuracy, and has long-term stability. The utilisation of GNSS ground networks of continuous stations for operational weather and climate services is already in place in many nations in Europe, Asia, and America under different initiatives and organisations. In Africa, the situation appears to be different. The focus of this paper is to assess the conditions of the existing and anticipated GNSS reference network in the African region for meteorological applications. The technical issues related to the implementation of near-real-time (NRT) GNSS meteorology are also discussed, including the data and network requirements for meteorological and climate applications. We conclude from this study that the African GNSS network is sparse in the north and central regions of the continent, with a dense network in the south and fairly dense network in the west and east regions of the continent. Most stations lack collocated meteorological sensors and other geodetic observing systems as called for by the GCOS Reference Upper Air Network (GRUAN) GNSS Precipitable Water Task Team and the World Meteorological Organization (WMO). Preliminary results of calculated zenith tropospheric delay (ZTD) from the African GNSS indicate spatial variability and diurnal dependence of ZTD. To improve the density and geometry of the existing network, countries are urged to contribute more stations to the African Geodetic Reference Frame (AFREF) program and a collaborative scheme between different organisations maintaining different GNSS stations on the continent is recommended. The benefit of using spaced based GNSS radio occultation (RO) data for atmospheric sounding is highlighted and filling of geographical gaps from the station-based observation network with GNSS RO is also proposed.

This book investigates tropospheric delays, one of the main error sources in Global Navigation Satellite Systems (GNSS), and its impact plays a crucial role in near real-time weather forecasting. Accessibility and accurate estimation of this parameter are essential for weather and climate research. Advances in GNNS application has allowed the measurements of Zenith Tropospheric Delay (ZTD) in all weather conditions and on a global scale with fine temporal and spatial resolution. However, GPS data are not always available for a full 24-hour period. Using a soft computing technique such as Adaptive Neuro-Fuzzy Inference System (ANFIS) as a new alternative, the ZTD can be determined by using the surface meteorological data as inputs. The estimation and prediction of ZTD value are presented in this book.

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

On-board cooperative spacecraft relative navigation fusing GNSS with vision

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.

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution would be hard, and GNSS accuracy cannot always achieve millimeter level to satisfy the requirement of house monitoring. Static level is a precision instrument for measuring elevation difference and its variations, with a precision up to sub-millimeter level. It could be integrated with GNSS to improve the positioning accuracy in height direction. However, the existing integration of GNSS and static level is mostly on a respective results level. In this study, we proposed a method of integrating GNSS and static level observations tightly to enhance the GNSS positioning performance. The hardware design and integration mathematic model in data processing were introduced, and a group of experiments were carried out to verify the performance in positioning with and without the static level observation constraints. It found that the vertical monitoring measurement results of static level can achieve less than 1 mm. The GNSS ambiguity resolution performance can be improved by incorporating the measurement of static level into GNSS positioning equation as external constraints, and the precision of GNSS float solutions was significantly improved. Finally, the static level constraint can further improve the accuracy of the fixed solution from about 2 cm to better than 2 mm in vertical direction, which is even better than the accuracy in horizontal directions with about 3–6 mm with the static level constraint. The tight combination data processing algorithm can significantly improve the working efficiency, accuracy, and reliability of the application of dangerous house monitoring.

This study explores mathematical models for calculating key operational parameters of a ship loader, including the linear position (P os), boom yaw angle (θy), boom elevation angle (θel), and boom position (Dbp), using (x,y,z) Global Navigation Satellite System (GNSS) coordinates along with heading (θh) and pitch angle (θp) data from a dual-antenna GNSS receiver. Additionally, the study introduces a collision avoidance system where machine positions are transmitted to a central Programmable Logic Controller (PLC), which generates a point cloud through geometric calculations to assess the distances between machines. Experimental results are presented, comparing the positions obtained from encoder instruments and demonstrating the effectiveness of the collision avoidance system.

The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision.

In Japan, precipitation has recently become more localized, concentrated, and intense. Extreme precipitation has caused a rapid land deformation, which results in ground water levels and loss of building stability. For the appropriate maintenance and renewal of infrastructure, including the design method of underground structures, the utilization of remote sensing technology which can always observe the ground deformation over a wide range with high accuracy is expected. This research presents the results of long-term continuous land deformation (subsidence or uplift) monitoring and using Global Navigation Satellite System (GNSS) and Consecutive Differential Interferometric Synthetic Aperture Radar (DInSAR) technique in Yokohama, Yokosuka and Miura cities, Kanagawa Prefecture, Japan. We adopted the Consecutive DInSAR method with the small spatial and short time baseline between satellites. The satellite data used thirty-three pairs of Sentinel-1 data in descending orbit from December 27, 2017 to November 17, 2020, and twenty-two Sentinel-1 data pairs in ascending orbit from June 26, 2019 to October 30, 2020. The validation of the SAR images used the daily coordinate values of the GNSS control point provided by the Geographical Survey Institute (GSI). As a result of the analysis, the estimated land displacement and GNSS data agreed well in Yokohama, Yokosuka, and Miura cities. The RMSE in descending orbit of Yokohama, Yokosuka and Miura cities were found to be 0.41 cm, 0.49 cm, and 0.49 cm, respectively and the RMSE in ascending orbit of Yokohama, Yokosuka and Miura cities were found to be 0.68 cm, 0.53 cm, and 0.58 cm, respectively, demonstrating its capability to analyze the land displacement monitoring over under different topographical conditions. In the future, this research result is expected to applicable in the field of disaster monitoring and environmental change observation which continuously require high accuracy observation over a wide area.

This paper presents one of the solutions to the problem of multipath propagation and effects on Global Navigation Satellite Systems (GNSS) signals in urban canyons. GNSS signals may reach a receiver not only through Line-of-Sight (LOS) paths, but they are often blocked, reflected or diffracted from tall buildings, leading to unmodelled GNSS errors in position estimation. Therefore in order to detect and mitigate the impact of multipath, a new ray-tracing model for simulation of GNSS signals reception in urban canyons is proposed - based on digital 3D maps information, known positions of GNSS satellites and an assumed position of a receiver. The model is established and validated using experimental, as well as real data. It is specially designed for complex environments and situations where positioning with highest accuracy is required - a typical example is navigation for blind people.

One of the most important physical properties of the soil is its mechanical strength. Increasing soil mechanical strength will lead to increments in the draft, fuel consumption, work duration and equipment wear, but will follow the reduction in the root growth. Awareness of the soil cone index (as a criterion of the arable soil compaction) in order to production management in connection with the soil physical properties have high importance, especially in precision farming. On the other hand, finding the methods and models that be able to create the best function or model to estimate the soil cone index at the least cost and use of available data are crucial for researchers. The aim of this study was predicting arable soils cone index values by effective parameters on the soil cone index, including bulk density, soil moisture content and soil electrical conductivity by using Fuzzy and neuro-fuzzy systems. In this study, for measurement and determination of the influencing factors on the soil cone index value, the experimental design was the factorial experiment based on a randomized complete block design with five replications. The experimental field had three types of loam, sandy loam and loamy sand soils. The modeling of soil cone index was performed by using effective parameters such as bulk density, moisture content and soil electrical conductivity in the fuzzy and adaptive neuro-fuzzy inference system (ANFIS) systems. For fuzzification of input and output parameters, the linguistic variables, including very low (VL), low (L), medium (M), high (H) and very high (VH) were used. Since determining the type and number of membership functions was conducted experimentally, the triangular membership function for both input and output variables was used due to the high accuracy and convenience in system design. In ANFIS model, 80% and 20% of total data were considered as training and test data, respectively. The numbers of membership functions were selected 5 for each input parameters. ANFIS training was done by the hybrid method. The average of coefficients of determination (R2) were obtained 80.1% and 97.9% for the fuzzy and ANFIS models, respectively. The obtained model through ANFIS presented the high accuracy of 2.54% rather than the fuzzy models with the accuracy of 9.68%. Also in comparison with regression models, ANFIS model has high accuracy and can be used to estimate the soil cone index in agricultural land.

The neutral atmosphere (or troposphere) causes refraction in radio frequency signals, which results in errors in Global Navigation Satellite Systems (GNSS) measurements. In meteorology, this effect can represent important measurements of the concentration of atmospheric constituents, especially in regions where conventional high-altitude atmospheric sounding (radiosondes) cannot be performed. There are two GNSS techniques used for this. In the first one, GNSS receivers are located on terrestrial stations that provide estimates of the vertically integrated moisture content (Precipitable Water Vapor - PWV). In the second case, receivers are in space platforms, which obtains profiles of atmospheric pressure, temperature and humidity, known as GNSS radio occultation. These measurements have significant potential for nowcasting applications (30 minutes in advance) of extreme precipitation events (>35 mm). This paper presents a review of the state of the art in the synergy between Geodesy and Meteorology for modeling the neutral atmosphere (neutrosphere), its effect on GNSS positioning and in the estimation of atmospheric constituents, and their applications. Furthermore, it offers the improvements and new challenges developed in modeling the delay for high accuracy positioning.

Global navigation satellite system (GNSS) _based multistatic radar is explored for target localization in this paper. Since any point on the earth can be illuminated by a minimum of 4 satellites of each GNSS constellation at any time, GNSS_based passive radar can be inherently considered multistatic radars. In this paper, we focus on the target localization accuracy analysis and CRLB is applied to evaluate the localization accuracy. Two situations, target localization with different number of satellites and target with different signal-to-noise ratio (SNR), are performed to analyze the localization accuracy. Simulation results indicate that a higher localization accuracy can be guaranteed with more satellites. Meanwhile, a higher accuracy can be obtained once the variation range of the echo SNR obtained by different satellites is small.

While Global Navigation Satellite Systems (GNSS) have been the primary Positioning, Navigation, and Timing (PNT) solution since their advent, new technologies are being sought to address the limitations of GNSS. For the past few years, the strongest contender to offer global PNT solutions has been the use of Low Earth Orbit (LEO) satellites, not only to overcome the GNSS limitations but also to improve the positioning accuracy and coverage given the proliferation of new, more-demanding applications. The present work aims at shedding light on the LEO theoretically achievable positioning accuracy and interference robustness with respect to GNSS, depending on the choice of the carrier frequency, constellation design, hybridization with GNSS, and available satellite transmission power. The analysis uses a semi-analytical approach with 192,000 Monte Carlo runs, employing an in-house satellite constellation simulator to model 400 users across Europe operating in five representative outdoor scenarios, and for 480 different instances of satellite positions in time. This semi-analytical study provides the necessary insights to derive clear design takeaways depending on the choice of the aforementioned parameters. A moderate Effective Isotropic Radiated Power (EIRP) of 50 dBm is shown to be sufficient for achieving high accuracy outdoors, operating at the C-band in the urban scenarios where GNSS typically struggles. The most cost-effective hybrid LEO plus GNSS solutions come from the combination of 1) ‘CentiSpace-like’ plus BeiDou, and 2) ‘Çelikbilek 1’ plus GPS and Galileo, where LEO satellites drive performance in difficult environments while GNSS provides stability in nominal conditions. The findings suggest using the ‘Çelikbilek 1’ constellation with a 50 dBm EIRP at a 5 GHz carrier frequency or higher as the most effective system design.

Android has been providing global navigation satellite system (GNSS) raw measurement data since the release of the Android operating system Nougat (N) in 2016. Users can use the GNSS measurements to directly estimate their accurate location. Studies are being actively conducted to improve the accuracy of user locations down to the decimeter level using GNSS raw measurements in Android smartphones and by applying methods such as Kalman filter (KF) and real-time kinematics (RTK). Android smartphones use planar inverted-F antennas (PIFAs) owing to hardware limitations. The PIFA structure is susceptible to multipath errors; therefore, significant noise is introduced into the raw GNSS measurements. In addition, there are software limitations. Further, the Duty Cycle functions in Android smartphones prioritize communication, which is the primary function of smartphones, over navigation during processor allocations. When a Duty Cycle is enabled, the antenna tracking the satellite to receive carrier measurements is forcibly disconnected and repeatedly reconnected. However, studies have shown that cycle slips still occur, even after limiting the Duty Cycle function, owing to the limitations of the smartphone antenna, and the utilization of carrier measurements is still challenging. In addition, not every smartphone manufacturer offers seamless support for carrier measurement. Therefore, Conducting RTK research using carrier measurements of smartphones is difficult. Previous studies have addressed the limitations of smartphones by receiving satellite signals using high-performance antennas on roofs of high-rise buildings, in ideal environments, and rebroadcasting the signals to smartphones from rebroadcasting rooms. In contrast to carrier measurements, Doppler measurements are not affected by cycle slips. As shown in Fig. 1, Doppler measurements in Android smartphones are less accurate than carrier measurements in the absence of cycle slips. However, the accuracy of Doppler measurements is excellent compared to the accuracy of code measurements. In this study, we applied a code–Doppler smoothing method that combines the advantages of code and Doppler measurements in Android smartphones. Fields such as flight formation, autonomous driving, and car navigation require accurate distance between objects. Therefore, sensors that estimate distance, such as LiDAR and radar, are typically required. The relative positioning method can be used to accurately estimate the distance between objects using only satellite measurements without these sensors. In this method, the difference between the two pseudoranges is determined. Subsequently, an accurate relative distance between two receivers can be estimated using this baseline vector and the position of the satellite. In our previous study, we improved the distance estimation accuracy using raw GNSS measurements from an Android smartphone and the two methods previously mentioned. The methods were verified by conducting an experiment using receivers of different performance levels in static and dynamic environments, but not in an ideal environment such as the rooftop of a high-rise building. In this study, relative positioning accuracy was improved by using the extended KF and GNSS raw measurements from an Android smartphone. Furthermore, the code–Doppler smoothing method was applied based on the characteristics of smartphone measurements. This method of precision relative positioning using Android smartphones is anticipated to be useful because of its simple and convenient usage without the need for additional equipment.