Accuracy Of Global Positioning System Research Articles

Skillful prediction of length of day one year ahead in multiple decadal prediction systems

Despite a small amplitude, Length of Day (LOD) change, which varies from one year to another due to changes in Atmospheric Angular Momentum (AAM), determines the accuracy of Global Positioning System (GPS) time calculation. In this study, we examine the prediction skill of LOD and AAM in nine decadal prediction systems archived for the Decadal Climate Prediction Project. A persistence and rebound in LOD prediction skill at one year or longer lead time is found in most models. A poleward propagation of AAM anomaly via wave-mean flow interaction is also qualitatively well reproduced. This long-lead prediction of LOD and AAM is attributed to reliable predictions of the El Niño–Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (QBO), the former being more systematically related than the latter. This result indicates that the improved ENSO prediction and atmospheric wave-mean flow interaction may lead to better prediction of LOD, AAM and related extratropical climate in the decadal prediction systems.

Comparative assessment of the feasibility and validity of daily activity space in urban and non-urban settings.

Activity space research explores the behavioral impact of the spaces people move through in daily life. This research has focused on urban settings, devoting little attention to non-urban settings. We examined the validity of the activity space method, comparing feasibility and data quality in urban and non-urban contexts. Overall, we found that the method is easily implemented in both settings. We also found location data quality was comparable across residential and activity space settings. The major differences in GPS (Global Positioning System) density and accuracy came from the operating system (iOS versus Android) of the device used. The GPS-derived locations showed high agreement with participants' self-reported locations. We further validated GPS data by comparing at-home time allocation with the American Time Use Survey. This study suggests that it is possible to collect daily activity space data in non-urban settings that are of comparable quality to data from urban settings.

Low-Earth-Orbit Satellites and Robust Theory-Augmented GPS/Inertial-Navigation-System Tight Integration for Vehicle-Borne Positioning

Positioning by means of the Global Positioning System (GPS) is a traditional and widely used method. However, its performance is affected by the user environment, such as multi-path effects and poor anti-interference abilities. Therefore, an Inertial Navigation System (INS) has been integrated with GPS to overcome the disadvantages of GPS positioning. INSs do not rely on any external system information and has strong autonomy and independence from the external environment. However, the performance of GPS/INS is visibly degraded in low-observability GPS environments (tall buildings, viaducts, underground tunnels, woods, etc.). Fortunately, with the emergence of Low-Earth-Orbit (LEO) satellites in recent years, the constellation configuration can be extended with the advantages of lower orbits, greater speeds, and richer geometric structures. LEO improves the geometric structure between users and satellites and provides many more observations. Meanwhile, a robust theory approach is applied that can restrain or remove the impact of low-accuracy observations. In this study, we applied LEO data and a robust theory approach to enhance the GPS/INS tight integration. To verify the effectiveness of this method, a set of vehicles and simulated LEO data were analyzed. The results show that robust Kalman filtering (RKF) provides a visible enhancement in the positioning accuracy of GPS/INS integration. This effectively restrains the mutation error and has a smoothing effect on the positioning results. In addition, the addition of LEO data significantly improves the positioning accuracy of a sole GPS and GPS/INS integration. The GPS/LEO/INS integration has the highest positioning accuracy, with Root-Mean-Square Errors (RMSEs) of the north, east, and vertical positions of 2.38 m, 1.94 m, and 2.49 m, respectively, which corresponds to an improvement of 30.21%, 47.43%, and 34.13% compared to sole GPS-based positioning and 8.60%, 17.24%, and 12.14% when compared to the GPS/INS mode. Simultaneously, the simulation results show that LEO and INSs can improve the positioning performance of GPS under GPS-blocked conditions.

Curating maternal, neonatal and child health (MNCH) datasets from a hospital’s catchment area in Nigeria between 2014 and 2019

In this Data Note, we present details regarding Maternal, Neonatal, and Child Health (MNCH) datasets sourced directly from patients' medical records. These datasets consist of 538 maternal, 720 neonatal, and 425 child records, all collected at St Luke’s General Hospital in Anua, Uyo, Nigeria, spanning from 2014 to 2019. Variables included in the datasets are:  Maternal {patient number, date of visit, gender, age, class of patient, address, LGA, diagnose, symptom, prescription, blood pressure (mm Hg), temperature (degree centigrade), weight (Kg), latitude, longitude, elevation, (MSL), date record, GPS Accuracy (m)}; Neonatal {patient number, date of visit, gender, age, class of patient, address, LGA, symptom, health status, height (cm), weight (Kg), latitude, longitude, elevation (MSL), date record, GPS Accuracy (m)}; and Child Health {patient number, date of visit, gender, age, class of patient, address, LGA, diagnose, health history, temperature (degree centigrade), weight (Kg), latitude, longitude, elevation (MSL), date record, GPS accuracy (m)}. The purpose of sharing these datasets is to provide a resource for researchers interested in their potential reuse, whether for analysis, research, quality assurance, policy formulation, decision-making, patient safety, or other purposes. The datasets also include location information obtained through GPS (Global Positioning System) data from the study area, facilitating spatiotemporal analysis. We outline the methods used for curating the datasets, including the protocol for selecting and processing variables. To protect patient privacy, certain personal details such as names were replaced with unique patient numbers generated using Microsoft Excel. Furthermore, specific patient information, including addresses/locations, date of visit, latitude, longitude, elevation, and GPS accuracy, has been restricted for privacy reasons. Readers interested in accessing restricted data can make a formal request to the corresponding author (see data restriction statement). The curated datasets are available at the Open Science Framework.

An Efficient Online Offline Data Collection Software Solution for Creating Reference Population to Established Genomic Selection Under Small Holders’ Dairy System in India

This article as part of an Enhanced Genetics Project assumes special significance in the light of emergence of electronic mode of data collection system. Pen and paper method of data collection although very familiar to field people, but is generates the errors and requires digitization and data cleaning. The software solution surveying is easier mode of ensuring uniformity in data collection, reducing errors and better storage and analysis of data. In this regard, this article gives a clearer picture of the digital tablet based data collection system employed part of the Enhanced Genetics Project across 49 selected Data Recorders (Data Enumerators) in Maharashtra, Uttar Pradesh, Jharkhand, Punjab, Odisha, Gujarat, Andhra Pradesh, Rajasthan, and Bihar. The manual outlines the processes to be fulfilled by the digital mobile based solution, the user interface involved and the software and hardware infrastructure to support such a platform independent system. In this project we have enrolled the 33,293 dairy animals and collecting there production, reproduction, body weight data, as well as, collecting the baseline & herd management information of that farm. This system increase Data Accuracy GPS (Global Positioning System) enabled data collection system for genomic evolution. This is very unique software solution developed for to collect the structured phenotype data of the dairy animals in Indian conditions on realtime basis. As well, show case the robust data collection system for the Genomic Selection for dairy animal’s initiative. This sub-system also focused on the various activities and operations related to molecular biological laboratory. In this software solution system the two major feedback systems for actively participation of the farmers were participation certificate and Graphical Animal Ranking system for the farmer and animal. The locational data collection with seamless interfacing with the user and the data repository for Genetic Improvement Data collection system. Data capture system was quite successful in gathering field phenotype data for Genetic Improvement Program in the complex dairy production system like India.

Modern GPS diagnostic technique to determine and map soil hardpan for enhancing agricultural operation management

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.

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Many bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that ‘clutter echoes’ originating from vegetation can convey information on the environment they were recorded in—despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The Global Positioning System (GPS) location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.

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 Novel CSINR Technique for Accurate and Precise GPS Communication by Geographical Centric Self-learning Nodes

Since its introduction, the Global Positioning System (GPS) is finding many countless, useful, and emergency applications, focused mainly on track. As the technology is advancing day by day and the best feature of GPS, which does not, relies on mobile signal to work, making it a feasible feature to incorporate into other devices as functionalities. By adopting GPS to a system, accurate mapping and geographical labeling can be obtained. GPS works better with the coordination of nodes and requires centralized monitoring and a reporting system. As it is a known fact that a demerit follows merit anywhere else, In GPS also, the major attention is required to make the nodes to success in mapping the intermediate space between agent node used for reporting and the remaining nodes of a cluster, where satellite and node coordination can be possible integer ambiguity technique. Many researchers have proposed solutions to the aforementioned problem; unfortunately still today the proposed methods are weaker in achieving lesser time delay of Total Electron Content (TEC). The proposed Centric Self-Learning Interconnected Nodes Reading (CSINR) technique is novel in terms addressing the intermediate nodes failing to label the inter-connected object spaces between reporting agents and nodes using integer ambiguity technique for node co-ordination and using a dedicated GPS prediction-based clock system, which predicts precise and accurate mapping between interconnected nodes. Based on the information shared between among the network managers a separate pseudo-connected network will be formed and further this network will be considered an interconnected nodes network. From the information calculated from temporal factors and clock offset the separate pseudo network is extracted by using the proposed CSINR technique. Add-on self-improvement is introduced to the proposed method by a self--learning feature to an individual join extract the principal rate of partisan neighbouring join to sustain accuracy in order consistent basis. An evaluation ratio of 97.43%, sensitivity of node occurrence is resulted as 92.78% and accuracy of 97.43% and 97.12% is achieved among a cluster of 32and 64 nodes respectively.

Device-based Real-time Single User Indoor Localization Using Internet of Things

Background: Localization is an important area of implementation of the internet of things based on Wireless Sensor Networks. Outdoor user tracking is possible using the global positioning system; however, the global positioning system accuracy decreases in indoor environments. To overcome this problem, the wireless sensor network is used in internet of things-based technology for localization. Objective: The wireless sensor network-based indoor localization is categorized into two categories; rangebased and range-free localization. In range-based localization, it first computes the relative distance and then calculates relative coordinates. In range-based techniques, distance and position are calculated. The internet of things-based localization uses the range-based and range-free techniques of a wireless sensor network to localize any object. The Light Dependent Resistor-based localization work has been proposed in previous research. In this research, the light-dependent resistor traces a person’s entry /exit event as the person switches ON/OFF lights of the building. However, it was not a sufficient effort to localize a person using light dependent resistor. To overcome the problem of light dependent resistor, the two PIR sensors have been used in each room along with one RFID based approach in this study. Methods: An indoor scenario has been considered in this study. The hardware setup has been configured to trace the user. When a user enters inside a building, he will switch on the lights, and the light sensor records the light intensity and gives some reading. The difference in the reading of the light sensor (before switching ON the light and after switching OFF the light) gives some clue about a user in an indoor scenario. Nevertheless, if the lights of many rooms remain switched ON, then the user cannot be localized using the above method. In order to sort out this ambiguity of light sensors, two passive infrared sensors in each room along with one radio frequency identification-based model have been proposed in the present study. Implementing the single-user localization using a light-dependent resistor sensor becomes erroneous if a person moves from one room to another and the lights get turned ON/OFF. Moreover, the LDR-based model is affected by sunlight during the daytime. Results: As the implementation of passive infrared sensors along with one RFID-based localization technique gives 93% efficiency, the light-dependent resistor-based localization system gives an efficiency of 35%. Conclusion: The light-dependent resistor-based approach is prone to more errors because the user may enter multiple rooms while the lights of each room remain ON. To overcome this problem, a passive infrared sensor and radio frequency identification-based approach for a single user indoor localization has been proposed. The proposed techniques are easy and cost-effective for implementation. The results show that the proposed technique provides better localization accuracy than a light-dependent sensor-based technique for single-user indoor localization.

Unsupervised Ethograms of a Vulnerable Bird Species: The Red-Footed Falcon in Northern Italy

Behavioral and movement ecology have quickly advanced as a result of the development of biotelemetry devices and analytical techniques. Global positioning system (GPS) transmitters assist scientists in gathering location and movement data at detailed spatial and temporal resolutions. Machine-learning methods can then be applied to GPS data to provide insights into the ecological mechanisms of animal behavior and movements. By means of accurate GPS data-loggers, in 2019, 2020, and 2021, we tracked 8 red-footed falcons at the two largest colonies in Italy. We collected 13,484 GPS points and used recently introduced machine-learning methodology Unsupervised Animal Behaviour Examiner (UABE) to deduce the regular, nested, and hourly ethograms of the tracked individuals. We found clear and significant patterns of the red-footed falcons’ behaviors on monthly, daily, and hourly bases. Our study is a step forward in advancing the knowledge of this threatened species, and provides a baseline assessment of the current behavioral patterns of this red-footed falcon population, with which results of future studies can be compared to detect potential behavioral changes that act as early warnings of increased human disturbance.

Evaluation of BDS/GPS Multi-Frequency RTK Positioning Performance under Different Baseline Lengths

The BeiDou Navigation Satellite System (BDS) is fully operational and provides positioning, navigation, and timing services to users worldwide. To comprehensively evaluate the BeiDou-3 Navigation Satellite System (BDS-3) global real-time kinematic (RTK) positioning performance, five sets of IGS/MEGX stations with different baseline lengths are selected in this research, and the visibility of current BDS-3, BDS-2+BDS-3, and Global Positioning System (GPS) system satellites are analyzed. The single frequency, dual-frequency, and triple-frequency positioning accuracy as well as ambiguity fixing rate under short baseline and long baseline are also analyzed. The experimental results show that the positioning accuracies of B1C, BII, L1, and B3I single-frequency bands were about the same, while for band B2a it was lower. For the short baseline dual-frequency RTK positioning mode, the accuracy of BDS-3 (B1C/B2a), BDS-3 (B1I/B3I), triple-frequency BDS-3 (B1C/B2a/B3I), and GPS (L1L2) is comparable and slightly better than that of BDS-3 (B1I/B3I). With the increase in baseline length, the advantages of dual-frequency BDS-3 (B1C/B2a) and triple frequency BDS (B1C/B2a/B3I) are more obvious, with triple-frequency BDS-3 (B1C/B2a/B3I) having the best positioning accuracy. In terms of ambiguity fixing performance, dual-frequency BDS-2+BDS-3 (B1I/B3I) and dual-frequency GPS (L1L2) have the highest ambiguity fixing rate. The ambiguity fixing rate of dual-frequency BDS-3 (B1C/B2a) and triple-frequency BDS-3 (B1C/B2a/B3I) can be higher than 90% within 100 km. In the case of positioning using only the BDS-3 system, the triple-frequency BDS-3 (B1C/B2a/B3I) is superior to both the dual-frequency BDS-3 (B1I/B3I) and dual-frequency BDS-3 (B1C/B2a) in terms of both positioning accuracy and ambiguity fixing rate. The BDS-2+BDS-3 (B1I/B3I) dual-frequency RTK, which has reached a level comparable to GPS, can provide global users with real-time centimeter-level differential positioning services.

Performance of Multi-GNSS in the Asia-Pacific Region: Signal Quality, Broadcast Ephemeris and Precise Point Positioning (PPP)

Since BeiDou Navigation Satellite System (BDS) and Japan’s Quasi-Zenith Satellite System (QZSS) have more visible satellites in the Asia-Pacific region, and navigation satellites of Global Positioning System (GPS), Galileo satellite navigation system (Galileo), and GLONASS satellite navigation system (GLONASS) are uniformly distributed globally, the service level of multi-mode Global Navigation Satellite System (GNSS) in the Asia-Pacific region should represent the best service capability. Based on the observation data of 10 Multi-GNSS Experiment (MGEX) stations, broadcast ephemeris and precision ephemeris from 13 to 19 October 2021, this paper comprehensively evaluated the service capability of multi-GNSS in the Asia-Pacific region from three aspects of observation data quality, broadcast ephemeris performance, and precision positioning level. The results show that: (1) the carrier-to-noise-density ratio (C/N0) quality of the GPS and Galileo is the best, followed by BDS and GLONASS, and QZSS is the worst. GPS, BDS-2, GLONASS, and QZSS pseudorange multipath values range from 0 to 0.6 m, while Galileo system and BDS-3 pseudorange multipath values range from 0 to 0.8 m. (2) In terms of broadcast ephemeris accuracy, BDS-3 broadcast ephemeris has the best orbit, and the three-dimensional (3D) Root Mean Square (RMS) is 0.21 m; BDS-2 was the worst, with a 3D RMS of 1.99 m. The broadcast ephemeris orbits of GPS, Galileo, QZSS, and GLONASS have 3D RMS of 0.60 m, 0.62 m, 0.83 m, and 1.27 m, respectively. For broadcast ephemeris clock offset: Galileo has the best performance, 0.61 ns, GLONASS is the worst, standard deviation (STD) is 3.10 ns, GPS, QZSS, BDS-3 and BDS-2 are 0.65 ns, 0.75 ns, and 1.72 ns, respectively. For signal-in-space ranging errors (SISRE), the SISRE results of GPS and Galileo systems are the best, fluctuating in the range of 0 m–2 m, followed by QZSS, BDS-3, Galileo, and BDS-2. (3) GPS, BDS, GLONASS, Galileo, GPS/QZSS, and BDS/QZSS were used for positioning experiments. In static PPP, the convergence time and positioning accuracy of GPS show the best performance. The positioning accuracy of GPS/QZSS and BDS/QZSS is improved compared with that of GPS and BDS. In terms of kinematic PPP, the convergence time and positioning accuracy of GPS/QZSS and BDS/QZSS are improved compared with that of GPS and BDS. In addition to GLONASS and Galileo systems, the other combinations outperformed 3 cm, 3 cm, and 5 cm in the east, north, and up directions.

Comparison of COSMIC and COSMIC-2 Radio Occultation Refractivity and Bending Angle Uncertainties in August 2006 and 2021

We compare the random error statistics (uncertainties) of COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate, C1) and COSMIC-2 (C2) radio occultation (RO) bending angles and refractivities for the months of August 2006 and 2021 over the tropics and subtropics using the three-cornered hat method. The uncertainty profiles are similar for the two RO missions in the troposphere. However, a higher percentage of C2 profiles reach close to the surface in the moisture-rich tropics, an advantage of the higher signal-to-noise ratio (SNR) in C2. C2 uses signals from both GPS (Global Positioning System) and GLONASS Global Navigation System Satellites (GNSS). The GPS occultations show smaller uncertainties in the stratosphere and lower mesosphere (30–60 km) than the GLONASS occultations, a result of more accurate GPS clocks. Therefore, C2 (GPS) uncertainties are smaller than C1 uncertainties between 30–60 km while the C2 (GLONASS) uncertainties are larger than those of C1. The uncertainty profiles vary with latitude at all levels. We find that horizontal gradients in temperature and water vapor, and therefore refractivity, are the major cause of uncertainties in the tropopause region and troposphere through the violation of the assumption of spherical symmetry in the retrieval of bending angles and refractivity.

Prototype of Bridge Navigational Watch Alarm System Equipped Obstacle Warning System Based on Image Processing and Real-Time Tracking

Ships are sea transportation that is often used in Indonesia. However, who would have thought that the transportation would become a case because of the frequent occurrence of accidents. This has been proven from data from the KNKT (National Transportation Safety Committee) which noted that from 2012 to 2017 there had been an increase in accidents in the waters. In fact, according to the National Search and Rescue Agency (Basarnas) in 2020 there have been 878 incidents with victims reaching 4658 people. In this final project, the author makes a prototype of BNWAS (Bridge Navigational Watch Alarm System) equipped with obstacle warning using image processing with the otsu method strengthened by thresholding-based segmentation with inverse technique (TsTN), distance detection using the triangle similarity method, real-time tracking with GPS (Global Positioning System) and the entire system can be observed on the Android application. The Final Project performs several analyzes including performance analysis by calculating the accuracy of the BNWAS alarm system, image detection accuracy, distance detection accuracy, GPS accuracy, overall system testing accuracy and packet loss. The accuracy of each system is very good because the error is below 2%, while for overall system testing has a very good performance with a delay of 179.8 ms and 0% packet loss.

Development of a Smartphone Application Serving Pavement Management Engineers

Determining the presence and location of pavement defects is vital to the timely maintenance of road infrastructure. Many applications exist for the purpose of tracking user-reported pavement distresses and roadway hazards, but few leverage the capabilities of modern smartphones. This pilot study aims to establish proof of concept that a custom-built smartphone application possesses the ability to utilize onboard smartphone sensors to collect global positioning system (GPS) and vibrational data with accuracy and sensitivity suitable for detecting pavement defects. An application was developed for Android and iOS that utilizes smartphone GPS, accelerometer, and gyroscope sensors to sample and log location, vibrational, and rotational data. The datasets obtained by the application are used to quantitatively establish the application’s ability to detect abnormalities in sampled data when in the presence of pavement defects or abnormal road conditions versus a baseline noise margin under no pavement defects. Additionally, the true sampling frequencies of both platforms are established to compare against user-specified sampling frequencies to determine true sampling performance. Furthermore, the datasets obtained from the Android and iOS platforms are compared to establish that there are differences in sensor hardware used that affect the sensor responses. Results from analysis of the datasets support the conclusion that the application successfully samples smartphone sensors, which can be used to detect defects in pavement when mounted to a vehicle windshield, as well as having the ability to provide relatively accurate and precise location data, given commercial smartphone GPS accuracy limitations.

Assessing the Kinematic GPS Positioning Performance Under the Effect of Strong Ionospheric Disturbance Over China and Adjacent Areas During the Magnetic Storm

AbstractThe 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.

Study protocol: prevalence of low energy availability and its relation to health and performance among female football players

Enduring low energy availability (LEA) is associated with several potentially serious physiological and mental conditions. LEA has been found highly prevalent among female elite athletes within endurance sports, thus hampering...

GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

Epidemiology of Underweight among Infants in Rural Burkina Faso.

ABSTRACT.Infant undernutrition is thought to contribute to growth failure and mortality. We evaluated the patterns in underweight in a population-based sample of children aged 1–11 months in rural northwestern Burkina Faso. Data were collected during the baseline assessment of a community-randomized trial evaluating mass azithromycin distribution in Nouna District, Burkina Faso. A door-to-door census was undertaken for all households in all communities. Infants aged 1–11 months were weighed for weight-based dosing in the trial and their weights were used to calculate weight-for-age Z-scores (WAZ). Underweight was defined as WAZ ≤ 2. We evaluated the age patterns in WAZ and underweight by demographic, seasonal, and geographic characteristics. Of 7,109 infants, 6,077 had accurate weight and global positioning system (GPS) coordinate data (85.5%). Infants were a median of 6 months old (interquartile range [IQR] 3–8) and 48.4% were female. Mean WAZ was −0.68 (SD 1.6) and 19.0% were underweight. The WAZ decreased with increasing age, and the prevalence of underweight increased from 2.5% among 1-month-olds to 27.6% among 11-month-olds. Underweight was more common among boys than girls (22.1% among boys versus 15.6% among girls). Improved latrine use by the household was associated with increased WAZ, and this effect was stronger in male compared with female infants. Given the large burden of underweight among infants, interventions addressing undernutrition should specifically include infants.