Is GPS telemetry location error screening beneficial?

The geomagnetic storm caused numerous disturbances of the ionosphere, particularly, plasma irregularities, which can affect the accuracy of Global Positioning System (GPS) positioning. This study quantifies the impacts of the ionospheric disturbance on GPS precise point positioning (PPP) solutions during extreme space weather events by taking advantage of 260 GPS stations in China. The results show that the average positioning error in China increased significantly during the ionospheric disturbance on 08 Sep 2017. By comparing the kinematic GPS‐PPP (3‐D) error distribution map and the ROTI index map, it is found that the ionospheric irregularities are one of the main reasons for degrading the GPS positioning accuracy. The statistical analysis results show that when the ionospheric scintillation index S4 is greater than 0.4, the average kinematic GPS‐PPP error calculated by the station is higher than 0.8 m.

Accurate, reliable, and robust global positioning system (GPS) localization is desirable for many navigation applications. Unfortunately, it is challenging for a single GPS receiver to always provide accurate positioning solutions. In urban environments, intermittent signal availability leads to degraded GPS signal tracking and position estimation of the single GPS receiver. In addition, equipment malfunction of the single GPS receiver results in inaccurate navigation solutions. This paper presents a deeply coupled multireceiver vector tracking (MRVT) architecture that improves the reliability and robustness of GPS signal tracking and position estimation. MRVT jointly tracks GPS signals received by multiple GPS receivers, mitigating GPS signal tracking disruptions, improving the reliability of GPS localization in periods of intermittent signal availability. In addition, the MRVT receiver is more robust to equipment malfunctions than the single GPS receiver. We implemented an MRVT receiver using commercial radio frequency front ends and our PyGNSS software. We experimentally validated the reliability of our MRVT receiver in periods of intermittent GPS availability experienced in downtown San Francisco. Our MRVT receiver exhibited consistent GPS signal tracking and position estimation as compared to vector tracking. In addition, we experimentally validated the robustness of our MRVT receiver to the failure of a single GPS receiver.

IMPROVING GPS ACCURACY USING NEURO-FUZZY SYSTEM

A reliable subtraction of seasonal signals from the Global Positioning System (GPS) position time series is beneficial for the accuracy of derived velocities. In this research, we propose a two-stage solution of the problem of a proper determination of seasonal changes. We employ environmental loading models (atmospheric, hydrological and ocean non-tidal) with a dominant annual signal of amplitudes in their superposition of up to 12 mm and study the seasonal signal (annual and semi-annual) estimates that change over time using improved singular spectrum analysis (ISSA). Then, this deterministic model is subtracted from GPS position time series. We studied data from 376 permanent International GNSS Service (IGS) stations, derived as the official contribution to International Terrestrial Reference Frame (ITRF2014) to measure the influence of applying environmental loading models on the estimated vertical velocity. Having removed the environmental loadings directly from the position time series, we noticed the evident change in the power spectrum for frequencies between 4 and 80 cpy. Therefore, we modelled the seasonal signal in environmental models using the ISSA approach and subtracted it from GPS vertical time series to leave the noise character of the time series intact. We estimated the velocity dilution of precision (DP) as a ratio between classical Weighted Least Squares and ISSA approach. For a total number of 298 out of the 376 stations analysed, the DP was lower than 1. This indicates that when the ISSA-derived curve was removed from the GPS data, the error of velocity becomes lower than it was before.

Summary Until very recently there have been no digital data from satellites for studying events that occur at scales of 10–1000 m2 over large areas (100–100 000 ha). Many phenomena of interest to ecologists, such as impacts of selective logging on forest processes, occur over large extents but at local scales. Here we report results from a pilot project to evaluate through visual interpretation the potential of newly available 1‐m panchromatic and 4‐m multi‐spectral data from the IKONOS satellite, for studying forest structure, dynamics and logging impacts in logged and old‐growth tropical moist forest. The study area, the Mil Madeireira Itacoatiara Ltda site of Precious Woods Amazon, near Itacoatiara, Amazonas, Brazil, is managed using reduced‐impact logging practices to minimize environmental impacts, and thus represents a lower bound for logging impacts in tropical rain forests. The IKONOS image was georeferenced using uncorrected global positioning system (GPS) locations for 10 control trees whose crowns were clearly visible in the image. The root mean square error (RMSE) of the geometric transformation was 4 m, while the mean crown diameter of 50 randomly chosen trees in old‐growth forest was 9·4 m. The fact that the RMSE was less than half the average crown diameter implies that it will usually be possible to locate from the ground crowns that are distinct on the image, given sufficiently accurate GPS locations. IKONOS data are well suited for evaluating and monitoring logging impacts. Many impacts of logging were clearly observable in the image, including major and some minor roads, logging patios and larger logging gaps. Smaller extraction roads and logging gaps were not observable. Many individual trees were distinct on the IKONOS image, indicating that it is now feasible to conduct demographic studies of tropical rain forest canopy trees based on repeated satellite observations. Linking these remotely sensed data to ground data will require improved GPS positions, because it is currently difficult to obtain accurate GPS readings in tropical rain forest understoreys. Synthesis and applications. IKONOS 1‐m and 4‐m data were found to be useful for identifying individual trees as well as some logging management features in a tropical moist forest in central Amazonia. These data will have many applications for research and management of intervened and old‐growth tropical forests, including planning and assessment of logging activities, as well as monitoring adherence to certification criteria such as those of the Forest Stewardship Council. Rapid development of these applications will come from building on existing data on forest structure and function, and by fostering collaborations between remote sensing scientists, ecologists and natural resource managers.

The purpose of this research is to develop and apply the "Recurrent S_CMAC_GBF (RSCMAC) [2] to enhance the accuracy of Global Positioning System (GPS). The performance is implemented and tested by a FPGA chip. In the research, the previous work has accomplished outstanding performance by using RSCMAC to predict GPS receiver static error to improve the error of the Differential-GPS. The simulation result shows 10 times accuracy is improved - GPS error is improved from larger than 20M to less than 2M. This paper employs previous research result to whole area prediction (non single fixed position), and then implements the performance by a FPGA chip. The key point is to commercialize the prototype design and its development. The most advantage of this project is to improve inexpensive GPS receiver positioning errors to increase the economic benefit of higher accuracy.

Monitoring of gully erosion on the Loess Plateau of China using a global positioning system

Global positioning system (GPS) telemetry units are now small enough to be deployed on terrestrial and semi-aquatic turtles. Many of these GPS units use snapshot technology which collects raw satellite and timestamp data during brief periods of data recording to minimize size. We evaluated locations from snapshot GPS units in stationary tests and on wood turtles (Glyptemys insculpta) in northeastern Minnesota. Stationary GPS units were placed in wood turtle habitat to evaluate location accuracy, fix success rate, and directional bias. The GPS fix success rate and accuracy were reduced in closed canopy conditions and when the stationary GPS unit was placed under a log to simulate wood turtle hiding behavior. We removed GPS location outliers and used a moving average calculation to reduce mean location error in stationary tests from 27 m (SD = 38) to 10 m (SD = 8). We then deployed GPS units and temperature loggers on wood turtles and collected 122,657 GPS locations and 242,781 temperature readings from 26 turtles from May to September 2015 and 2016. Location outliers accounted for 12% of locations when the GPS receiver was on a turtle. We classified each wood turtle location based on the GPS location and by comparing temperature profiles from river, sun, and shaded locations to the temperature logger on the turtle. We estimated that wood turtles were on land 68% (SD = 12) of the time from May to September. The fix success rate for land locations was 38% (SD = 9), indicating that wood turtles often use habitats with obstructed views of the sky. Mean net daily movement was 55 m (SD = 192). Our results demonstrate that snapshot GPS units and temperature loggers provide fine-scale GPS data useful in describing spatial ecology and habitat use of semi-aquatic turtles.

Investigation of GPS positioning accuracy during the seasonal variation

In shaded areas, Global Positioning System (GPS) signals may suffer significant degradation, making the measurements to be contaminated by different kinds of errors. These errors such as outliers arising due to multipath and line-of-sight signals can degrade the accuracy of GPS positioning. Moreover, the poor geometric constellation of satellites that the receiver is tracking may cause inverse problems, resulting in unstable and unreliable GPS positioning solutions. To tackle these problems, an iterative robust regularization method (IRRM) for the GPS positioning is proposed in this article. The IRRM employs the Cholesky factorization method to preclude computing the inverse of matrices, making it numerically stable and be easily implemented in engineering. Also included in this work is the derivation of the sufficient condition for the IRRM. Performance comparisons of the proposed algorithm with the least squares, the Cholesky factorization method for iterative least squares (CM), and the robust estimation are demonstrated via two static positioning problems. The experimental results, presented herein, exhibit the improved performance of the IRRM over conventional algorithms.

When we examine the impacts of space weather on society, do we really understand the risks? Can past experiences reliably predict what will happen in the future? As the complexity of technology increases, there is the potential for it to become more fragile, allowing for a single point of failure to bring down the entire system. Take the Global Positioning System (GPS) as an example. GPS positioning, navigation, and timing have become an integral part of daily life, supporting transportation and communications systems vital to the aviation, merchant marine, cargo, cellular phone, surveying, and oil exploration industries. Everyday activities such as banking, mobile phone operations, and even the control of power grids are facilitated by the accurate timing provided by GPS. Understanding the risks of space weather to GPS and the many economic sectors reliant upon it, as well as how to build resilience, was the focus of a policy workshop organized by the American Meteorological Society (AMS) and held on 13–14 October 2010 in Washington, D. C. The workshop brought together a select group of policy makers, space weather scientists, and GPS experts and users. Arif Alikhan, assistant secretary for policy development in the U.S. Department of Homeland Security, kicked off the meeting by stressing that authorities should see GPS and resilience through an all-hazards lens rather than plan for each emergency scenario on a case-by-case basis. Whether a GPS signal error or disruption is caused by a solar storm, an intentional jammer, or some other source, government and industries need to plan to respond to the incident and restore capability as quickly as possible, although, Alikhan remarked, “The number of potential scenarios is infinite, and we have neither the time nor resources to plan against them.” Alikhan suggested that risk managers conceptualize the scope of critical assets in need of protecting through a resilience and risk analysis framework. He recommended that decision makers take an inventory of all functions provided for by their institutions“ use of GPS. Next, through risk-informed prioritization, planners can ascertain whether a backup is really needed for any services provided by GPS. “In this fiscal environment,” Alikhan said, “we will need to shrink the haystack, or size of the problem, in order to be effective.” Because the interdependencies of critical infrastructures are not fully appreciated by the public and private sectors, he stressed that a mix of governmental, commercial, and international stakeholders should develop plans and procedures to make systems more resilient. The remainder of the workshop was organized around discussions in three sessions: (1) identifying the vulnerability of GPS technologies and services to space weather, (2) identifying how this vulnerability affects users who depend on GPS, and (3) developing opportunities and policies for building resilience. In the first session, participants agreed that the vulnerability of GPS needs to be defined for different segments (e.g., satellites, signals, users). While some understanding of user needs exists, the definition of user requirements needs more clarification because space weather events will affect some technologies more than others. Also, a threat assessment would be valuable to best understand how severe the impacts might be and what mitigation options are available. With the recent news that Google™ is building self-driving robotic cars that depend on GPS, trying to capture possible future vulnerabilities is extremely challenging. During a debate on how much to stress the impact of a solar flare as massive as the 1859 Carrington event, many warned that while the public should be made aware of such a potential occurrence, it should not govern planning, since such a doomsday scenario is uncertain and expected to happen only very infrequently. When discussing in the second session how users of GPS are affected, participants discovered that communicating with GPS stakeholders and users is a challenge because many do not understand the impacts of adverse space weather. In particular, many of the service providers and end users of GPS were not in business during the previous solar maximum and therefore are not familiar with space weather hazards. To help remedy this, outreach approaches were discussed, and some participants argued that the space weather community should anticipate the needs of the users. Others instead urged the space weather community to deal only with the service providers—not end users—and ask them rather than tell them what they need. Despite this debate, there was broad agreement that space weather researchers should develop better measurements and predictions of the ionosphere. In addition, participants agreed that scintillation specifications and predictions would be useful to many users of GPS. Improved predictions would be possible by installing more dual-frequency stations and scintillation monitors, they noted. The workshop concluded with a discussion of policy options for building resilience. Participants debated what measures could be taken, such as developing new policies, investing in research and development, changing industry practices, and public and private sector collaboration. Scott Pace, director of George Washington University“s Space Policy Institute, stressed in his presentation that stakeholders and disaster planners should focus on three aspects: prediction, detection, and mitigation. Better space physics/heliophysics science is needed, and it was recommended that this be a top priority for the Solar and Space Physics decadal survey, which sets future nationwide research targets and is currently under review by the U.S. National Research Council. Findings and recommendations from the workshop will be published in a report by AMS. The results will be widely distributed to various stakeholders, including policy makers, GPS service providers, federal agencies, research communities, and international organizations. Genene Fisher is a senior policy fellow in the American Meteorological Society's Policy Program. E-mail: [email protected]

Among the undesirable effects of soil compaction is a measurable reduction in plant growth and crop yield. The prevailing belief is that compacted tillage pans are caused by repetitive farming practices, heavy tractors, tillage tools, and field traffic. This experiment was conducted to determine and map the hardpan layers across an agricultural field through advanced technologies of precision agriculture. These valuable techniques such as data logger, yield map, and data analysis of performance indicators were linked with accurate global positioning systems (GPS) datasets. These important technologies provided the farmers and helped them to identify and manage areas of the fields with higher compacted layers. Three ground speeds 4.3, 5.2, and 6.4 km h-1 were performed with two tillage depths 25 and 40 cm of a chisel plow. The effects of these two factors were studied to determine slippage percentage, field productivity, traction power, and fuel consumption. For the first shallow 25 cm depth, the results showed that increasing the speed from 4.3 to 5.2 and then to 6.4 km h-1 led to a significant increase in slippage percentage from 7.22 to 10.35 and then to 12.63%, respectively. Increasing the speed increases field productivity from 0.547 to 0.663 then to 0. 749 ha hour-1, and tractive power increases from 9.44 to 11.74, then to 13.24 hp. As a result, there was a significant increase in the fuel consumption rate from 18.44 to 20.15, then to 22.27 L hour-1, respectively. Changing the depth from 25 to 40 cm and increasing the practical speed from 4.3 to 5.2 and then to 6.4 km h-1 led to a significant increase in slippage percentage from 10.14 to 12.77 and then to 15.27%, and a significant increase in field productivity from 0.446 to 0.568 and then to 0.640 ha hour-1, respectively. This led to a significant increase in traction power from 12.72 to 13.36, then to 15.87 hp. Increasing the speed also brought a significant increase in fuel rate from 22.14 to 23.54 and then to 26.14 L ha-1, respectively. Based on this study, it was concluded that the use of this powerful approach was a useful methodology to reflect, determine, specify, and manage the regions of induced and hardpan zones by means of dataset analyses provided by the GPS for the desired field.

The traditional approach to both earthquake and Global Positioning System (GPS) location problems in a homogeneous half-space produces a nonlinear relationship between a set of known positions, seismic stations or GPS satellites, and an unknown point, an earthquake hypocenter or GPS receiver. Linearization, followed by an iterative inversion, is typically used to solve both problems. Although sources and receivers are swapped in the earthquake and GPS location problems, the obser- vation equation is the same for both, due to the principle of reciprocity. Consequently, the mechanical part of the solution of the equations is the same and single-step closed- form solutions for the GPS location problem, such as the Bancroft algorithm, can also be used to solve for earthquake hypocenters in a homogeneous half-space. This article applies the Bancroft algorithm to synthetic and real data for the Charlevoix seismic zone and compares the location of ∼1200 events estimated with both the Bancroft algorithm and HYPOINVERSE. The Bancroft algorithm shows quantifiable improve- ments in accuracy compared with traditional methods. We also show how tools com- monly used by the GPS community, such as the geometric dilution of precision, can be used to better estimate the precision of the results obtained by a seismic network.

It is well known that in high-speed movements, the positioning accuracy of global positioning system (GPS) receivers decreases drastically. The models presented so far to describe high-speed motion do not represent the state of the system precisely, so positioning accuracy with the methods based on these models is not appropriate. Here, a comprehensive method is proposed to solve the accuracy issue of the single-frequency GPS receiver at high-speed motions without increasing the computational complexity. Suitable modelling of the GPS receivers at high-speed motion, using the sequential extended Kalman filter, correct determination of the process noise covariance matrix and accurately estimating the variance of the observations are the basics of the proposed approach. Simulations with different data and motion scenarios (at speeds from 100 to 7300 m/s) show that the proposed method, while not increasing the computational cost, improves the accuracy of positioning more than 70% when compared to the conventional methods.

Global Positioning System (GPS) units in telemetry collars provide an unbiased and precise estimate of animal locations. Under ideal conditions at least 50% of locations are expected to be within 40 m in uncorrected mode GPS, and within 5 m in differential mode GPS. When the collar was placed under open sky, most locations were 3-dimensional locations that could be differentially corrected. Under hardwood canopies with leaves on, the frequency of 3-dimensional locations decreased, the frequencies of failed location attempts and 2-dimensional locations increased, and the precision of GPS locations decreased. We compared the precision of each GPS mode by calculating uncorrected mode and differential mode locations from the same pseudo-range and ephemeris data. We varied the number of satellites used in the location solution to simulate the effect of decreased satellite acquisition due to canopy cover on precision of locations. Precision of locations increased if signals from >4 satellites were used to calculate the location in uncorrected mode and in differential mode. We found that 2-dimensional locations were almost as precise as 3-dimensional positions if the altitude of the GPS unit was known. If the altitude used to calculate a 2-dimensional location was within 50 m of the actual collar altitude, the precision of 2-dimensional differential mode locations was better than 3-dimensional uncorrected mode locations. If the error in altitude was 100 or 150 m, then 50% of 2-dimensional differential mode locations were within 70 m and 95% were within 185 m of the true location. We used GPS locations from collars placed in different cover types and on free-ranging moose (Alces alces) to determine the effect of season, time of day, rainfall, and cover type on GPS performance. On free-ranging moose the collar GPS unit found ≥4 satellites on 52% of location attempts, >50% of locations were 3-dimensional, and >24% of locations were 2-dimensional. Precise tracking of individual animals in all weather throughout the year is possible with GPS telemetry.