Real-time Global Positioning System Research Articles

Twenty-Two Years of GPS Monitoring at Rabaul Caldera, a Narrative History

It has long been recognised that volcanoes deform as fluids migrate, or change pressure in fractures and reservoirs within the volcano or in the crust below and around them. Calderas in particular have been shown to deform in complex and often major ways. The Rabaul Caldera is a type example of a caldera that undergoes complex and occasionally rapid deformation. This was first recognised by visual observations, and by the 1970s these movements were being monitored by traditional surveying techniques. Between 1972 and 1994, the centre of the caldera was uplifted by approximately 2 m. Following the 1994 eruption, it was indirectly found that parts of the caldera were uplifted ~6 m in the final hours before the eruption. It was realized that ‘real-time’ monitoring of the uplift may have given a better warning that an eruption was imminent. Traditional surveying techniques are time consuming; in the late 1990s, the only option for real-time monitoring was a Global Positioning System (GPS). By early 2000, a real-time GPS system was working at Rabaul Volcanological Observatory (RVO). Twenty-two years of continually recording differential GPS or Global Navigational Satellite System (GNSS) has proven the technique to be of immense importance. Often it has been the only parameter showing that unrest is happening. At times, inflation and deflation have warned of impending activity or recorded the emptying of the system; at other times, patterns of deformation have been more difficult to interpret. The technique has proven its worth in monitoring the status or general ‘health’ of the caldera, but for more precise forecasts it can only form part of an integrated monitoring system. Current testing of much cheaper receivers and improvements in telemetry mean the technique may soon be available for the more remote volcanoes of Papua New Guinea.

Visual Place Recognition of Robots via Global Features of Scan-Context Descriptors with Dictionary-Based Coding

Self-localization is a crucial requirement for visual robot place recognition. Particularly, the 3D point cloud obtained from 3D laser rangefinders (LRF) is applied to it. The critical part is the efficiency and accuracy of place recognition of visual robots based on the 3D point cloud. The current solution is converting the 3D point clouds to 2D images, and then processing these with a convolutional neural network (CNN) classification. Although the popular scan-context descriptor obtained from the 3D data can retain parts of the 3D point cloud characteristics, its accuracy is slightly low. This is because the scan-context image under the adjacent label inclines to be confusing. This study reclassifies the image according to the CNN global features through image feature extraction. In addition, the dictionary-based coding is leveraged to construct the retrieval dataset. The experiment was conducted on the North-Campus-Long-Term (NCLT) dataset under four-seasons conditions. The results show that the proposed method is superior compared to the other methods without real-time Global Positioning System (GPS) information.

Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification

In this paper, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.

A Robust and Precise LiDAR-Inertial-GPS Odometry and Mapping Method for Large-Scale Environment

In this article, we propose a system that can fuse inertial measurement unit (IMU), light detection and ranging (LiDAR), and intermittent global positioning system (GPS) measurements to achieve high precision localization and mapping in large-scale environment. The iterated error-state Kalman filter is used to fuse IMU and LiDAR measurements to estimate relative motion information quickly. Based on the factor graph, LiDAR, GPS measurements, and loop closure are transformed into constraints for joint optimization to construct global map. A real-time GPS outlier detection method based on the IMU preintegration theory and residual chi-square test is designed. In addition, the use of robust kernel is supported to implicitly reduce the impact of undetected GPS outliers on the system. A LiDAR-based loop-closure detector is designed, which can search for a pair of point clouds with similar geometric information. Importantly, the performance of the proposed method is compared with state-of-the-art methods through a series of experiments. The experimental results show that the method is robust and accurate to complete the task of localization and mapping in large-scale environment.

GPS data Mining at Signalized Intersections for Congestion Charging.

Nowadays more private car trips have caused worse congestion due to the Covid-19 pandemic in many cities. Congestion charging is one of the taxes that is levied on vehicle owners to reduce urban traffic congestion. One of the most important reasons congestion charging is not accepted by the public is the high cost. Monitoring the state of traffic congestion in real time requires a lot of expensive installations. The purpose of this paper is to make congestion charging more accurate and acceptable using artificial intelligent algorithm. Massive real-time Global Positioning System (GPS) data provides new data for road congestion charging. The queuing length at intersections is an important measurement for the degree of traffic congestion, and it is also the basis for road congestion pricing. GPS positioning cannot provide sufficient position accuracy for lane identification of vehicles. In this study, a comprehensive model consisting of a real-time lane identification model and a real-time queue length estimation model is developed based on the traffic shockwave theory using GPS data. The comprehensive model can identify the lane where the queuing vehicle is located and estimate the real-time queue length of the lane. The proposed models were evaluated using field-collected data in Guangzhou, China. The testing results show that the proposed comprehensive model can identify lanes and estimate queue lengths with satisfactory accuracy. The model proposed in this paper provides real-time data for road dynamic pricing in a cost-effective way, which can promote the implementation of congestion charging in cities.

Utilization of Indonesia’s regional ionosphere model to improve the accuracy of GPS measurements to support disaster mitigation studies

Ionospheric can cause severe degradation of GPS (Global Positioning System) functionality and decrease coordinate accuracy. Increasing the precision of GPS station coordinates will improve accuracy in many applications. Many applications can use GPS for deformation studies, such as geodynamic studies, active fault studies, volcanic deformation monitoring, land subsidence studies, and hazard mitigation studies. We can use global ionospheric correction to produce better coordinates by utilizing post-processing GPS data. With the increasing number of GPS stations in Indonesia, it is possible to develop regional ionosphere models. This study computes regional ionospheric models from real-time streaming GPS data and uses them in GPS processing. Regional ionospheric models can increase the accuracy of GPS station coordinates by 5% -10% compared to global ionosphere models (igsg and codg).

Real-Time Precise GPS Orbit and Clock Estimation With a Quasi-Orbit-Fixed Solar Radiation Pressure Model

<h3>Abstract</h3> Real-time precise global navigation satellite system (GNSS) orbit and clock products play a key role for real-time GNSS-based applications, both in the scientific and industrial communities. Different from the typical two-step procedure to generate orbit and clock solutions separately, we estimate the real-time orbit and clock products simultaneously using a Kalman filter. For this purpose, we developed a GNSS data processing software that can run in pseudo-real-time mode with RINEX files and is ready to run in real-time mode once given the real-time observation stream. Meanwhile, a quasi-orbit-fixed solar radiation pressure (SRP) model is developed. In order to verify the performance of the software and the new SRP model, several experiments with a global network of 60 tracking stations over a time span of three months were conducted to generate real-time Global Positioning System (GPS) orbit and clock products. Then, the results were assessed in terms of accuracy and efficiency, both critical for real-time precise GNSS applications. Compared to the International GNSS Service (IGS) final orbits, the real-time GPS orbit accuracy was 2.82 cm, 5.45 cm, and 5.47 cm in the radial, along-track, and cross-track components, respectively. The precision of the clock product in terms of standard deviation (STD) value was about 0.1 ns. Moreover, the average execution time per epoch was usually less than 1.0 s, which ensures the high efficiency of the processing.

Rapid Earthquake Source Description Using Variometric-Derived GPS Displacements toward Application to the 2019 Mw 7.1 Ridgecrest Earthquake

AbstractUsing near-field high-rate Global Positioning System (GPS) displacements to invert for earthquake fault slips in real time has the potential to improve the accuracy of earthquake early warning or tsunami early warning. For such applications, real-time retrieval of high-accuracy GPS displacements is essential. Here, we report on rapid modeling of the 2019 Mw 7.1 Ridgecrest earthquake with real-time GPS displacements derived from a variometric approach with readily available broadcast ephemeris. This method calculates station variations in real time by differencing continuous phase observations and does not rely on precise orbit and clock information. The phase ambiguity is also removed, and thus the method does not suffer from a relatively long convergence time. To improve the accuracy of variometric displacements, we use a local spatial filter to decrease the influence of residual errors that cannot be removed completely by the time difference. We invert for the centroid moment tensor, static fault slips, and fault rupture process from the derived displacements. Our results show that all inverted models are available within about 65 s after the origin time of the earthquake and are comparable with models inverted by real-time precise point positioning displacements. This study highlights the great value of variometric displacements for the rapid earthquake source description with only broadcast ephemeris.

Temporal Changes in Association Patterns of Cattle Grazing at Two Stocking Densities in a Central Arizona Rangeland

Simple SummaryMonitoring changes in the utilization of forages across rangelands can be time consuming and difficult with untrained personnel. The use of real time positioning for cattle is becoming commercially available with the improvements in technology. The objective of this case study was to identify the changes in livestock social associations and spatial location at two stocking densities throughout a six-week grazing period. Both pastures used similar sized herds with 35 and 29 animals tracked with global positioning systems set at 30-min intervals. A half-weight index value was calculated for each pair of tracked cattle to determine the proportion of time that cattle were within 75 m and 500 m of each other. Throughout the study, forage utilization increased from 5 to 24% and from 10% to 20% and forage mass decreased from 2601 kg ha−1 to 1828 kg ha−1 and 2343 kg ha−1 to 1904 kg ha−1, in the high stocking density pasture and low stocking density pasture, respectively. Utilization of forages throughout the trial forced cattle to disperse and travel further from water sources to find new feeds. Real-time GPS tracking has the potential to remotely detect changes in animal spatial association, identify when cows disperse, and improve recognition for the need of pasture rotation to avoid rangeland degradation.Proper grazing management of arid and semi-arid rangelands requires experienced personnel and monitoring. Applications of GPS tracking and sensor technologies could help ranchers identify livestock well-being and grazing management issues so that they can promptly respond. The objective of this case study was to evaluate temporal changes in cattle association patterns using global positioning system (GPS) tracking in pastures with different stocking densities (low stocking density [LSD] = 0.123 animals ha−1, high stocking density [HSD] = 0.417 animals ha−1) at a ranch near Prescott, Arizona. Both pastures contained similar herd sizes (135 and 130 cows, respectively). A total of 32 cows in the HSD herd and 29 cows in the LSD herd were tracked using GPS collars at location fixes of 30 min during a 6-week trial in the summer of 2019. A half-weight index (HWI) value was calculated for each pair of GPS-tracked cattle (i.e., dyads) to determine the proportion of time that cattle were within 75 m and 500 m of each other. Forage mass of both pastures were relatively similar at the beginning of the study and forage utilization increased from 5 to 24% in the HSD pasture and increased from 10 to 20% in the LSD pasture. Cattle in both pastures exhibited relatively low mean association values (HWI < 0.25) at both spatial scales. Near the end of the study, cattle began to disperse likely in search of forages (p < 0.01) and travelled farther (p < 0.01) from water than during earlier periods. Real-time GPS tracking has the potential to remotely detect changes in animal spatial association (e.g., HWI), and identify when cows disperse, likely searching for forage.

IMPLEMENTASI SISTEM MONITORING KOORDINAT LATITUDE DAN LONGITUDE BERBASIS INTERNET OF THINGS(IOT) SECARA REALTIME

Nowadays, the virtual world is not something new. The role of the virtual world relies heavily depend on communication to report the situation and condition as soon as the position of soldiers in the military world, to carrying out their assignments and training. To support this task, a tool for determining coordinates using HT (Handy Talky) was made by transmitting real-time GPS (Global Position System) data based on IoT (Internet of Things) using the internet protocol network as the transmission medium. With the GPS system, latitude and longitude data will be obtained from the GPS module. The results of data will be processed by the Microcontroller, which we use the Arduino Pro Micro module then sent to the Monitoring server which will be shown in the form of mapping and show coordinate data, observers can monitor the position of field personnel who are carrying out patrols according to their area.

Opportunities to Apply Precision Livestock Management on Rangelands

Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

Tracking and sensor-based detection of livestock water system failure: A case study simulation

Water is an important nutrient, and its continuous provision is a critical welfare issue for cattle grazing arid and semiarid rangelands. Time and labor are needed to monitor water availability, and automated monitoring systems are a costly input on expansive rangeland pastures. The objective of this study was to evaluate the potential of detecting water system failures using Global Positioning System (GPS) tracking and accelerometers, assuming the data could be monitored in real or near-real time. Water system failure was simulated in a 1 096-ha pasture in Arizona by placing metal panels around the only drinker for 4 h (0800−1200) on three occasions in 2018 and two occasions in 2019. Randomly selected cows (10 in 2018 and 23 in 2019) of the 120 cows in the pasture were tracked with GPS collars, and 7 (2018) and 10 (2019) of the tracked cows were fitted with triaxial accelerometers. Movement intensity measured by accelerometers was greater (P = 0.03) on the day of simulated water failure than on control days with available water. During simulated water failure, cows remained closer to water (P = 0.01) after approaching the drinker (< 150 m) compared with the control period the day prior. Cows typically went to the drinker, drank, and then traveled away from the drinker and rested. On simulated water-failure days, cows remained near the drinker (< 150 m from the tank) until the panels were removed and they could drink. Real-time GPS tracking with or without accelerometer data has the potential to remotely detect water system failure, which could reduce the time for managers to repair the water system and improve cattle well-being.

Sustainability of Public Transportation: An Examination of User Behavior to Real-Time GPS Tracking Application

Public transportation is an effective method of mobility that promotes cost-saving and is environmentally friendly. Poor public transport ridership in Malaysia is due to the unsatisfactory attitude of public transport users and inaccurate information on departure and arrivals. Sarawak, a state of Malaysia, is especially poor in ridership of public transport. A real-time Global Positioning System (GPS) tracking application (app) was found to be an effective tool to increase the ridership of public transport. Hence, a mobile app named UniBus was developed to enhance the ridership of public transport in Sarawak. The determinants that affect satisfaction and customer loyalty such as accessibility, reliability, comfort, safety, and security were all examined before and after the use of real-time GPS tracking app. The data was collected in Kuching, and targeted public transport users who used the UniBus app. The result indicated that all the mentioned variables were improved after using a real-time GPS tracking app. It is suggested that future studies can consider other factors such as service quality, availability, and perceived value as well as cover other states of Malaysia.

Application of high-rate GPS for earthquake rapid response and modelling: a case in the 2019 Mw 7.1 Ridgecrest earthquake

SUMMARYThe 2019 Mw 7.1 Ridgecrest earthquake opens an opportunity to investigate how soon we can produce a reliable fault geometry and subsequently a robust source model based on high-rate Global Positioning System (GPS) data. In this study, we conduct peak ground displacement (PGD) magnitude scaling, real-time centroid moment tensor (CMT) calculation and rapid kinematic slip inversion. We conclude that a four-station PGD warning with a magnitude of Mw 7.03 can be issued at 24 s after initiation of the rupture. Fast CMT inversion can initially recover the correct nodal planes at 30 s. The kinematic slip model reveals that the Mw 7.1 earthquake is a predominant dextral strike-slip event with both normal and thrust components resolved. The earthquake shows a bilateral rupture with a low propagation speed of ∼2.1 km s−1 and a slip maxima of ∼4 m. The total moment is 5.18 × 1019 N m (Mw 7.11). We further suggest that a reasonable source model will be available in a simulated real-time mode within 30 s after the earthquake occurring, without using full high-rate GPS waveforms. This research highlights the significance of high-rate GPS for rapid earthquake response and modelling of kinematic rupture, which is also generalized by the hypothetical real-time GPS analysis for the 2016 Mw 7.8 Kaikoura earthquake and the 2010 Mw 7.2 El Mayor-Cucapah earthquake.

DYNAMIC TRIP PLANNER FOR PUBLIC TRANSPORT USING GENETIC ALGORITHM

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper.

Covariance Analysis of Real-Time Precise GPS Orbit Estimated from Double-Differenced Carrier Phase Observations

The covariance of real-time global positioning system (GPS) orbits has been drawing attention in various fields such as user integrity, navigation performance improvement, and fault detection. The international global navigation satellite system (GNSS) service (IGS) provides real-time orbit standard deviations without correlations between the axes. However, without correlation information, the provided covariance cannot assure the performance of the orbit product, which would, in turn, causes significant problems in fault detection and user integrity. Therefore, we studied real-time GPS orbit covariance characteristics along various coordinates to effectively provide conservative covariance. To this end, the covariance and precise orbits are estimated by means of an extended Kalman filter using double-differenced carrier phase observations of 61 IGS reference stations. Furthermore, we propose a new method for providing covariance to minimize loss of correlation. The method adopted by the IGS, which neglects correlation, requires 4.5 times the size of the covariance to bind orbit errors. By comparison, our proposed method reduces this size from 4.5 to 1.3 using only one additional parameter. In conclusion, the proposed method effectively provides covariance to users.

Assessment of L5 frequency on real-time GPS orbit and clock estimation

Real-time precise Global Navigation Satellite System (GNSS) satellite orbit and clock products are the prerequisite of real-time GNSS-based applications. With the modernization of Global Positioning System (GPS) constellation and construction of other GNSS constellations, the number of GNSS satellites transmitting multi-frequency signals is increasing rapidly. While the benefit of multi-frequency to Precise Point Positioning (PPP) and Precise Clock Estimation (PCE) has been evaluated by many researchers, the impact of multi-frequency on orbit and clock estimation is still limited, especially in real-time mode. Different from the dual-frequency model, the Inter-Frequency Clock Bias (IFCB) must be treated carefully in the triple-frequency model. In this contribution, the triple-frequency model for real-time GPS satellite orbit and clock estimation is derived and the influence of L5 frequency observations on real-time GPS satellite orbit and clock are analyzed. With observation data from globally distributed stations spanning January 1 to 14 of 2018, real-time GPS orbit and clock are estimated with both dual-frequency and triple-frequency observations. Numerical experiments indicate that, compared to the International GNSS Service (IGS) final orbit, the L5 frequency observations improve the consistency of the normal component with about 0.2 cm, however lead to a decrease of consistency in the radial component with about 0.6 cm; the influence on the tangential direction varies with different number of stations. The potential reason for this phenomenon may partly be attributed to the absence of accurate antenna information for L5 frequency. However, due to the tight constraint of dynamic force model on orbit, the overall differences for each component is less than 1.0 cm, which means the contribution of the third frequency observation on the orbit is limited.

Unanticipated Uses of the Global Positioning System

Global Positioning System (GPS) instruments are routinely used today to measure crustal deformation signals from tectonic plate motions, faulting, and glacial isostatic adjustment. In parallel with the expansion of GPS networks around the world, several new and unexpected applications of GPS have been developed. For example, GPS instruments are now being used routinely to measure ground motions during large earthquakes. Access to real-time GPS data streams has led to the development of better hazard warnings for tsunamis, flash floods, earthquakes, and volcanic eruptions. Terrestrial water storage changes can be derived from GPS vertical coordinate time series. Finally, GPS signals that reflect on the surfaces below a GPS antenna can be used to measure soil moisture, snow accumulation, vegetation water content, and water levels. In the future, combining GPS with the signals from the Russian, European, and Chinese navigation constellations will significantly enhance these applications. ▪ GPS data are now routinely used to study the dynamics of earthquake rupture. ▪ GPS instruments are an integral part of warning systems for earth- quakes, tsunamis, flash floods, and volcanic eruptions. ▪ Reflected GPS signals provide a new source of soil moisture, snow depth, vegetation water content, and tide gauge data. ▪ GPS networks can sense changes in soil moisture, groundwater, and snow depth and thus can contribute to water resource assessments.

Adaptive turning control for an agricultural robot tractor

An adaptive turning algorithm for a four-wheel robot tractor in the headland is presented in this paper. The navigation sensors consisted of an inertial measurement unit and a real-time kinematic global positioning system (GPS). An objective function based on weights was used to create the navigation path, connecting by continuous primitives. The asymmetric steering mechanism was then taken into consideration with a vehicle model. To follow the path accurately, the slide movement of the robot and the steering rate were taken into account by estimating the turning radius in real time. In addition, the vehicle model was tuned based on the results of each turn. Therefore, the turning control algorithm was optimized on the basis of the specific conditions in the field. Field experiments showed that the robot tractor approached the next path with an average lateral deviation of 3.9 cm at a speed of 1.2 m/s during a turn. Compared to a conventional turning scheme, the time consumption and turning trajectory were decreased by 17% and 21%, respectively. Keywords: autonomous tractor, path planning, dynamic circle-back turning, switch-back turning, robot tractor, reinforcement learning DOI: 10.25165/j.ijabe.20181106.3605 Citation: Wang H, Noguchi N. Adaptive turning control for an agricultural robot tractor. Int J Agric & Biol Eng, 2018; 11(6): 113–119.

Development of a Real-Time Software-Defined GPS Receiver in a LabVIEW-Based Instrumentation Environment

The ubiquitousness of location based services (LBS) has proven effective for many applications such as commercial, military, and emergency responders. Software-defined radio (SDR) has emerged as an adequate framework for development and testing of global navigational satellite systems (GNSS) such as the Global Position System (GPS). SDR receivers are constantly developing in terms of acceleration factors and accurate algorithms for precise user navigation. However, many SDR options for GPS receivers currently lack real-time operation or could be costly. This paper presents a LabVIEW (LV) and C/C++ based GPS L1 receiver platform with real-time capabilities. The system relies on LV acceleration factors as well as other C/C++ techniques such as dynamic link library (DLL) integration into LV and parallelizable loop structures, and single input multiple data (SIMD) methods which leverage host PC multi-purpose processors. A hardware testbed is presented for compactness and mobility, as well as software functionality and data flow handling inherent in LV environment. Benchmarks and other real-time results are presented as well as compared against other state-of-the-art open-source GPS receivers.