Precise positioning with current multi-constellation Global Navigation Satellite Systems: GPS, GLONASS, Galileo and BeiDou.
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ABSTRACT:
SUBMITTER: Li X
PROVIDER: S-EPMC4321187 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:Multi- system and multi-frequency are two key factors that determine the performance of precise point positioning. Both multi-frequency and multi-system lead to new biases, which are not solved systematically. This paper concentrates on mathematical models of biases, influences of these biases, and positioning performance analysis of different observation models. The biases comprise the inter-frequency clock bias in multi-frequency and the inter-system clock bias in multi-system. The former is the residual differential code biases (DCBs) from receiver clock and satellite clock and usually occurs at the third frequency, the latter is the deviation of the receiver clock errors in different systems. Unified mathematical models of the biases are presented by analyzing the general formula of observation equations. The influences of these biases are validated by experiments with corresponding observation models. Subsequently, the experiments, which are based on the data at five globally distributed stations in Multi-Global Navigation Satellite System (GNSS) Experiment (MGEX) on day of year 100, 2018, assess positioning performance of different observation models with combination of frequencies (dual-frequency or triple- frequency) and systems (BeiDou Navigation Satellite System (BDS) or Global Positioning System (GPS)). The results show that the performances of triple-frequency models are almost as the same level as the dual-frequency models. They provide scientific support for the triple-frequency ambiguity-fixed solution which has a better convergence characteristic than dual-frequency ambiguity-fixed solution. Furthermore, the biases are expressed as an unified form that gives an important and valuable reference for future research on multi-frequency and multi-system precise point positioning.
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Project description:Aerial unmanned vehicles, so-called drones, present a paradigm shift away from the long-term use by scientists of manned aeroplanes and helicopters. This is evident from the number of research articles that focus on data obtained with drones. This article examines the use of aerial drones for scientific research in cryospheric regions, especially Antarctica and the Arctic. Specifically, it aims to provide insights into the choices and performance of global navigation satellite systems (GNSS) use for drones, including augmentation systems. Data on drone GNSS navigation and positioning in the context of scientific polar research have been scarce. Drone survey data obtained from polar scientists in April 2019 is the first representative sample from this close-knit global community across the specialisms of climatology, ecology, geology, geomorphology, geophysics and oceanography. The survey results derived from 16 countries revealed that 14.71% of scientists used GALILEO, 27.94% used GLONASS and 45.59% used GPS. Many used a combination of two or more GNSS. Multiple regression analysis showed that there is no strong relationship between a specific pattern of GNSS augmentation and greater positioning accuracy. Further polar drone studies should assess the effects of phase scintillation on all GNSS, therefore BEIDOU, GALILEO, GLONASS and GPS.
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Project description:Although precise point positioning (PPP) is a well-established and promising technique with the use of precise satellite orbit and clock products, it costs a long convergence time to reach a centimeter-level positioning accuracy. The PPP with ambiguity resolution (PPP-AR) technique can improve convergence performance by resolving ambiguities after separating the fractional cycle bias (FCB). Now the FCB estimation is mainly realized by the regional or global operating reference station network. However, it does not work well in the areas where network resources are scarce. The contribution of this paper is to realize an ambiguity residual constraint-based PPP with partial ambiguity resolution (PPP-PARC) under no real-time network corrections to speed up the convergence, especially when the performance of the float solution is poor. More specifically, the update strategy of FCB estimation in a stand-alone receiver is proposed to realize the PPP-PAR. Thereafter, the solving process of FCB in a stand-alone receiver is summarized. Meanwhile, the influencing factors of the ambiguity success rate in the PPP-PAR without network corrections are analyzed. Meanwhile, the ambiguity residual constraint is added to adapt the particularity of the partial ambiguity-fixing without network corrections. Moreover, the positioning experiments with raw observation data at the Global Positioning System (GPS) globally distributed reference stations are conducted to determine the ambiguity residual threshold for post-processing and real-time scenarios. Finally, the positioning performance was verified by 22 GPS reference stations. The results show that convergence time is reduced by 15.8% and 26.4% in post-processing and real-time scenarios, respectively, when the float solution is unstable, compared with PPP using a float solution. However, if the float solution is stable, the PPP-PARC method has performance similar to the float solution. The method shows the significance of the PPP-PARC for future PPP applications in areas where network resource is deficient.
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Project description:Positioning accuracy is a challenging issue for location-based applications using a low-cost global positioning system (GPS). This paper presents an effective approach to improving the positioning accuracy of a low-cost GPS receiver for real-time navigation. The proposed method precisely estimates position by combining vehicle movement direction, velocity averaging, and distance between waypoints using coordinate data (latitude, longitude, time, and velocity) of the GPS receiver. The previously estimated precious reference point, coordinate translation, and invalid data check also improve accuracy. In order to evaluate the performance of the proposed method, we conducted an experiment using a GARMIN GPS 19xHVS receiver attached to a car and used Google Maps to plot the processed data. The proposed method achieved improvement of 4-10 meters in several experiments. In addition, we compared the proposed approach with two other state-of-the-art methods: recursive averaging and ARMA interpolation. The experimental results show that the proposed approach outperforms other state-of-the-art methods in terms of positioning accuracy.
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Project description:Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.
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Project description:BackgroundMaternal exposures to traffic-related air pollution have been associated with adverse pregnancy outcomes. Exposures to traffic-related air pollutants are strongly influenced by time spent near traffic. However, little is known about women's travel activities during pregnancy and whether questionnaire-based data can provide reliable information on travel patterns during pregnancy.ObjectivesExamine women's in-vehicle travel behavior during pregnancy and examine the difference in travel data collected by questionnaire and global positioning system (GPS) and their potential for exposure error.MethodsWe measured work-related travel patterns in 56 pregnant women using a questionnaire and one-week GPS tracking three times during pregnancy (<20 weeks, 20-30 weeks, and >30 weeks of gestation). We compared self-reported activities with GPS-derived trip distance and duration, and examined potentially influential factors that may contribute to differences. We also described in-vehicle travel behavior by pregnancy periods and influences of demographic and personal factors on daily travel times. Finally, we estimated personal exposure to particle-bound polycyclic aromatic hydrocarbon (PB-PAH) and examined the magnitude of exposure misclassification using self-reported vs. GPS travel data.ResultsSubjects overestimated both trip duration and trip distance compared to the GPS data. We observed moderately high correlations between self-reported and GPS-recorded travel distance (home to work trips: r?=?0.88; work to home trips: r?=?0.80). Better agreement was observed between the GPS and the self-reported travel time for home to work trips (r?=?0.77) than work to home trips (r?=?0.64). The subjects on average spent 69 and 93 minutes traveling in vehicles daily based on the GPS and self-reported data, respectively. Longer daily travel time was observed among participants in early pregnancy, and during certain pregnancy periods in women with higher education attainment, higher income, and no children. When comparing self-reported vs. GPS data, we found that estimated personal exposure to PB-PAH did not differ remarkably at the population level, but the difference was large at an individual level.ConclusionSelf-reported home-to-work data overestimated both trip duration and trip distance compared to GPS data. Significant differences in PAH exposure estimates were observed at individual level using self-reported vs. GPS data, which has important implications in air pollution epidemiological studies.
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Project description:BackgroundPersonal exposure studies of air pollution generally use self-reported diaries to capture individuals' time-activity data. Enhancements in the accuracy, size, memory and battery life of personal Global Positioning Systems (GPS) units have allowed for higher resolution tracking of study participants' locations. Improved time-activity classifications combined with personal continuous air pollution sampling can improve assessments of location-related air pollution exposures for health studies.MethodsData was collected using a GPS and personal temperature from 54 children with asthma living in Montreal, Canada, who participated in a 10-day personal air pollution exposure study. A method was developed that incorporated personal temperature data and then matched a participant's position against available spatial data (i.e., road networks) to generate time-activity categories. The diary-based and GPS-generated time-activity categories were compared and combined with continuous personal PM2.5 data to assess the impact of exposure misclassification when using diary-based methods.ResultsThere was good agreement between the automated method and the diary method; however, the automated method (means: outdoors?=?5.1%, indoors other =9.8%) estimated less time spent in some locations compared to the diary method (outdoors?=?6.7%, indoors other?=?14.4%). Agreement statistics (AC1?=?0.778) suggest 'good' agreement between methods over all location categories. However, location categories (Outdoors and Transit) where less time is spent show greater disagreement: e.g., mean time "Indoors Other" using the time-activity diary was 14.4% compared to 9.8% using the automated method. While mean daily time "In Transit" was relatively consistent between the methods, the mean daily exposure to PM2.5 while "In Transit" was 15.9 ?g/m3 using the automated method compared to 6.8 ?g/m3 using the daily diary.ConclusionsMean times spent in different locations as categorized by a GPS-based method were comparable to those from a time-activity diary, but there were differences in estimates of exposure to PM2.5 from the two methods. An automated GPS-based time-activity method will reduce participant burden, potentially providing more accurate and unbiased assessments of location. Combined with continuous air measurements, the higher resolution GPS data could present a different and more accurate picture of personal exposures to air pollution.
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Project description:BackgroundThis is the first study, of which we are aware, to evaluate the feasibility and accessibility of simultaneous use of Global Positioning System (GPS) and saliva collection for biomarker assessment as an objective measure of stress physiology among sexual minority (lesbian, gay, bisexual, queer, and other non-heterosexual identities) individuals. The principal motivation for pairing GPS and saliva collection was to investigate how characteristics of the built and social environments along with participants' daily activity paths affect stress. This can contribute to a better understanding of health and health behaviors in the sexual minority community.MethodsA convenience sample of enrolled participants (N = 124) from Houston, Texas was asked to complete questionnaires, carry with them a GPS unit daily, and collect and store 6 samples of saliva at specific times across the span of a day prior to a second visit around one week later.ResultsOf 124 participants, 16 participants (12.90%) provided no useable GPS data and 98 (79.03%) provided at least 4 days of data. More than three-fourths (n = 98, 79.03%) also provided complete saliva samples.ConclusionsOur results show that the simultaneous use of GPS and saliva collection to assess sexual minority individuals' activity paths and stress level is feasible.
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Project description:In this paper, an anti-jamming method, which turns the single objective optimization problem into a multi-objective optimization problem by utilizing 2-norm, is proposed. The proposed jamming suppression method can reduce the wide nulls and wrong nulls problems, which are generated by the common adaptive nulling methods. Therefore a better signal-noise-ratio (SNR) can be achieved, especially when the jammers are close to satellite signals. It can also improve the robustness of the algorithm. The effectiveness of the proposed method is evaluated by simulation and practical outdoor experiments with the GPS L1 band C/A signals. The experimental results show that with the dedicated method, the nulls targeting at the corresponding jammers become narrower and the wrong nulls can be eliminated.
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