실내외 천이영역 적용을 위한 WLAN/GPS 복합 측위 알고리즘

Training prescription and monitoring of team-sport athletes rely on accurate quantification of player movement. Our aim was to determine the sensitivity, reliability and construct validity of measures derived from a wearable device incorporating Global Positioning System (GPS) and accelerometer technology to quantify the peak periods of rugby competition. Match movement data were collected from 30 elite and 30 sub-elite rugby union players across respective competitive seasons. Accelerometer and GPS measures were analysed using a rolling average to identify peak movement for epochs ranging from 5 to 600 seconds. General linear mixed modelling was used to quantify the effects of playing position and match-half on the peak movement and variabilities within and between players represented reliability of each measure. Mean positional differences and match-half changes were assessed via standardisation and magnitude-based decisions. Sensitivity of measures was quantified via evaluation of ("signal") and typical error of measurement ("noise"). GPS and accelerometer measures had poor sensitivity for quantifying peak movement across all epochs and both levels of rugby union competition (noise 4× to 5× the signal). All measures displayed correspondingly low reliability across most epochs and both levels of competition (ICC<0.50). Construct validity was evident in mean differences between playing positions and match halves that were consistent with expected activity profiles in rugby union. However, it was clear from the pattern of differences across epoch durations and levels of competition that GPS and accelerometer measures provided different information about player movement. The poor sensitivity and low reliability of GPS and accelerometer measures of peak movement imply that rugby union players need to be monitored across many matches to obtain adequate precision for assessing individuals. Although all measures displayed construct validity, accelerometers provided meaningful information additional to that of GPS. We recommend using accelerometers alongside GPS to monitor and prescribe match respresentative training.

Constraints on 2004 Sumatra–Andaman earthquake rupture from GPS measurements in Andaman–Nicobar Islands

We present analysis results for Global Positioning System (GPS) measurements made at sites in Costa Rica during two campaigns in February and July 1991. GPS solutions for 5 sites (Limon, Liberia, Bratsi, ETCG, and Vueltas) reveal significant horizontal and vertical displacements relative to their February positions. Horizontal displacements relative to Liberia, measured 244.7 ± 0.8, 89.2 ± 0.9, 12.4 ± 1.3, and 1.9 ± 0.9 cm at Limon, Bratsi, Vueltas, and ETCG respectively. Vertical displacements relative to Liberia measured 163 ± 2.1, 15.3 ± 3.0, −10.5 ± 4.4, and −0.6 ±2.1 cm at Limon, Bratsi, Vueltas, and ETCG respectively. We find differences in the GPS derived vertical and horizontal displacements compared to other types of geodetic measurements of uplift in the coastal region and their associated models. To address these differences we compute a dislocation model which fits the GPS measured displacements. A simple uniform planar slip model can not reconcile the differences between the coastal uplift data or the seismic moment, suggesting considerable complexity of the earthquake source.

Seismic Hazard at the New Madrid Seismic Zone

Effects of physical correlations on long-distance GPS positioning and zenith tropospheric delay estimates

TH-2 is China’s first short-range satellite formation system used to realize interferometric synthetic aperture radar (InSAR) technology. In order to achieve the mission goal of InSAR processing, the relative orbit must be determined with high accuracy. In this study, the precise relative orbit determination (PROD) for TH-2 based on global positioning system (GPS), second-generation BeiDou navagation satellite system (BDS2), and GPS + BDS2 observations was performed. First, the performance of onboard GPS and BDS2 measurements were assessed by analyzing the available data, code multipath errors and noise levels of carrier phase observations. The differences between the National University of Defense Technology (NDT) and the Xi’an Research Institute of Surveying and Mapping (CHS) baseline solutions exhibited an RMS of 1.48 mm outside maneuver periods. The GPS-based orbit was used as a reference orbit to evaluate the BDS2-based orbit and the GPS + BDS2-based orbit. It is the first time BDS2 has been applied to the PROD of low Earth orbit (LEO) satellite formation. The results showed that the root mean square (RMS) of difference between the PROD results using GPS and BDS2 measurements in 3D components was 2.89 mm in the Asia-Pacific region. We assigned different weights to geostationary Earth orbit (GEO) satellites to illustrate the impact of GEO satellites on PROD, and the accuracy of PROD was improved to 7.08 mm with the GEO weighting strategy. Finally, relative orbits were derived from the combined GPS and BDS2 data. When BDS2 was added on the basis of GPS, the average number of visible navigation satellites from TH-2A and TH-2B improved from 7.5 to 9.5. The RMS of the difference between the GPS + BDS2-based orbit and the GPS-based orbit was about 1.2 mm in 3D. The overlap comparison results showed that the combined orbit consistencies were below 1 mm in the radial (R), along-track (T), and cross-track (N) directions. Furthermore, when BDS2 co-worked with GPS, the average of the ambiguity dilution of precision (ADOP) reduced from 0.160 cycle to 0.153 cycle, which was about a 4.4% reduction. The experimental results indicate that millimeter-level PROD results for TH-2 satellite formation can be obtained by using onboard GPS and BDS2 observations, and multi-GNSS can further improve the accuracy and reliability of PROD.

It is unclear whether there is a difference between subjective evaluation and objective global positioning systems (GPS) measurement of running distance. The purpose of this study was to investigate if such difference exists. A total of 100 participants (51% men; median age, 41.5; body mass, 78.1 kg ±13.8 SD) completed a run of free choice, then subjectively reported the distance in kilometer (km). This information was subsequently compared with the distance derived from a nondifferential GPS watch using paired t-tests and Bland-Altman's 95% limits of agreement. No significant difference was found between the mean paired differences between subjective evaluations and GPS measurements (1.86%, 95% confidence interval = -1.53%; 5.25%, p = 0.96). The Bland-Altman 95% limits of agreement revealed considerable variation (lower limit = -28% and upper limit = 40%). Such variation exceeds the clinical error range of 10%. In conclusion, the mean running distance (km) is similar between self-reporting and GPS measurements. However, researchers should consider using GPS measurements in favor of subjective reporting of running distance because of considerable variation on an individual level.

Position determination using satellite navigation system has grown significantly. It provides geospatial with global coverage called GNSS (Global Navigation System Satellite). GNSS satellites consists of GLONASS, GPS (Global positioning system) and Galileo.GPS is the most commonly used system and it is known to its capability to determine 3D position on the surface of the earth. In order to determine the position, a GPS receiver must be able to receive signals from at least four GPS satellites. However, the determination of position in condensed areas such as tunnels, area surrounded by high rise buildings, highly forested and in other closely-knit areas is not achieved because satellite signals cannot reach the receiver in the above-mentioned areas and also others where the signals are reflected before being received by a GPS receiver. In this paper, we present the algorithm to fuse GPS and the inertial measurement unit (IMU) to enable positioning in the above-mentioned Condensed Areas. The standard deviations of the two measurements show that GPS-IMU is better than GPS alone, the standard deviation when satellite outages occurred is - 4.57475 for GPS-IMU measurements and 0.218675 for GPS observations. We presented the results in graphics and it shows that GPS measurements are easily disturbed by external influence such as multipath but GPS-IMU graphic is continuous and robust. The advantages and disadvantages of GPS and INS are complementary and make them work together to enable the accurate measurements in the areas mentioned above. Integration of INS and GNSS can be classified into three types, loosely coupled Kalman filter, tightly coupled Kalman filters and ultratight coupled Kalman filter. In this research we used loosely coupled Kalman filter and tightly coupled Kalman filters to combine GPS and INS in one system.

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.

We investigate how land measurements vary across three distinct land measurement techniques—farmers’ self-reported data, Global Positioning System (GPS) measurements, and Google estimates from remotely sensed satellite data—in Bhutan. The findings reveal a lack of significant differences between GPS and Google measurements across five key paddy-growing districts. However, the study identifies a noteworthy bias in farmers’ self-reported data relative to GPS measures. This bias is evident in both the levels and logarithmic representations of land area, with farmers tending to over-report for smaller-sized plots and underreport for larger-sized plots.

Comparing vertical total electron content from GPS, Bent and IRI models with TOPEX-Poseidon

This paper presents an adaptive learning method for data fusion in autonomous\ndriving vehicles. The localization is based on the integration of Inertial\nMeasurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning\nSystem (GPS) units in an adaptive Kalman filter (KF). The observability\nanalysis reveals that $i$) integration of a single GPS with IMU does not\nconstitute an observable system; $ii$) integration of two GPS units with IMU\nresults in a locally observable system provided that the line connecting two\nGPS antennas is not parallel with the vector of the measured acceleration,\ni.e., the sum of inertial and gravitational accelerations. The latter case\nmakes it possible to compensate for the error in the estimated orientation due\nto gyro drift and its bias without needing additional instruments for absolute\norientation measurements, e.g., a magnetic compass. Moreover, in order to cope\nwith the fact that GPS systems sometimes lose their signal and receive\ninaccurate position data, the self-tuning filter estimates the covariance\nmatrix associated with the GPS measurement noise. This allows the KF to\nincorporate GPS measurements in the data fusion process heavily only when the\ninformation received by GPS becomes reliably available.\n

We know that position accuracies from relative and static Global Positioning System (GPS) measurements are degraded by a variety of physical, hardware and software effects. As a result, users often take special precautions to minimise these effects and, hopefully, improve their accuracies when planning and making measurements. In this study, the effects of the baseline length (BL) and observation duration (OD) on vertical and horizontal positioning errors for relative, static GPS measurements were experimentally evaluated. In making this evaluation, 5 min to 24 hours GPS data, collected over a 15 day period, from the Southern California Integrated GPS Network (SCIGN), were analyzed. The root mean square (RMS) of the differences between the true and measured vertical (up/ellipsoidal height) and horizontal (north and east/latitude and longitude) positions of the included stations were computed and then used to create empirical error equations dependent upon the BL and OD. Considerations for the number of satellites (SVs) and the positional dilution of precision (PDOP) were included too by necessity. With these equations, GPS users can better estimate their vertical and horizontal position accuracies based on the known BLs, desired ODs, available SVs, and predicted PDOPs when planning a project, or have an external standard of comparison when evaluating their results.

Agricultural statistics derived from remote sensing data have been used primarily to compare land use information and changes over time. Nonclassical measurement error from farmer self-reports has been well documented in the survey design literature primarily in comparison to plots measured using Global Positioning System (GPS). In this paper, we investigate the reliability of remotely sensed satellite data on nonrandom measurement error and on agricultural relationships such as the inverse land size–productivity relationship and input demand functions. In our comparison of four Asian countries, we find significant differences between GPS and remotely sensed data only in Viet Nam, where plot sizes are small relative to the other countries. The magnitude of farmers’ self-reporting bias relative to GPS measures is nonlinear and varies across countries, with the largest magnitude of selfreporting bias of 130% of a standard deviation (2.2-hectare bias) in the Lao People’s Democratic Republic relative to Viet Nam, which has 13.3% of a standard deviation (.008-hectare bias). In all countries except Viet Nam, the inverse land size–productivity relationship is upwardly biased for lower land area self-reported measures relative to GPS measures. In Viet Nam, the intensive margin of organic fertilizer use is negatively biased by self-reported measurement error by 30.4 percentage points. As remotely sensed data becomes publicly available, it may become a less expensive alternative to link to survey data than rely on GPS measurement.

The International GPS Service (IGS) Ionosphere Working Group