Evaluation method of polynomial models' prediction performance for random clock error

Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO 2 ( FCO 2) represent the “true” flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include “tall tower” instrumentation), one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions. We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO 2, for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with R n ( H and LE) and PPFD ( FCO 2). For FCO 2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO 2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.

Precise Point Positioning (PPP) is an increasingly recognized precisely the GPS/GNSS positioning technique. In order to improve the accuracy of PPP, the error sources in PPP measurements should be reduced as much as possible and the ambiguities should be correctly resolved. The correct ambiguity resolution requires a careful control of residual errors that are normally categorized into random and systematic errors. To understand effects from two categorized errors on the PPP ambiguity resolution, those two GPS datasets are simulated by generating in locations in South Korea (denoted as SUWN) and Hong Kong (PolyU). Both simulation cases are studied for each dataset; the first case is that all the satellites are affected by systematic and random errors, and the second case is that only a few satellites are affected. In the first case with random errors only, when the magnitude of random errors is increased, L1 ambiguities have a much higher chance to be incorrectly fixed. However, the size of ambiguity error is not exactly proportional to the magnitude of random error. Satellite geometry has more impacts on the L1 ambiguity resolution than the magnitude of random errors. In the first case when all the satellites have both random and systematic errors, the accuracy of fixed ambiguities is considerably affected by the systematic error. A pseudorange systematic error of 5 cm is the much more detrimental to ambiguity resolutions than carrier phase systematic error of 2 mm. In the 2 nd case when only a portion of satellites have systematic and random errors, the L1 ambiguity resolution in PPP can be still corrected. The number of allowable satellites varies from stations to stations, depending on the geometry of satellites. Through extensive simulation tests under different schemes, this paper sheds light on how the PPP ambiguity resolution (more precisely L1 ambiguity resolution) is affected by the characteristics of the residual errors in PPP observations. The numerical examples recall the PPP data analysts that how accurate the error correction models must achieve in order to get all the ambiguities resolved correctly.

Navigation signals are the basis of satellite navigation systems, which determine the navigation systems' performance (such as tracking accuracy, anti-interference capability etc.), and are determined by some constraints (such as radio frequency resources, construction cost, technology level etc.). The aim of signal design is to select "proper" signals considering these above factors and make tradeoffs between performance and constraints. This paper will summarize the signal structure evolution of present satellite navigation and conclude some principles and issues which will be greatly valuable to our 2nd satellite navigation system. There's four main satellite positioning systems presently, the U.S. Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), the Europe Galileo satellite navigation system and the Chinese BD-1 satellite positioning system. Among these systems, the signal characteristics of GPS and Galileo are good reference to our 2nd satellite navigation system. The improvement of GPS signals is a main aspect of GPS modernization. The aim of GPS modernization is mainly to modernize its signal structure so as to improve the whole systems' performance. The signal structure of Galileo has been revised for several times, and its present signal is the results of long time signal design. From GPS modernization and Galileo signals status, we can summarize some key specifications of navigation signals and some technologies to support these specifications. Based on this, we'll discuss some problems which should be attentive in our country's 2nd satellite navigation system and give some suggestions. This paper is organized into five sections. The first section will give a brief introduction of present satellite navigation system and their main features. Section two will review the GPS and Galileo signals structure evolution, including GPS modernization, Galileo signals status and the comparison of two systems' signals. In the third section, the relationship between signal parameters and system performance will be analyzed, including RF signal characteristics, modulation type, ranging code (such as code type, length and rate) and navigation data. In section four we will discuss some inspiration and technologies concerning our 2nd satellite navigation system, such as the use of multiple frequency signals (ionospheric delay estimation and carrier ambiguity resolution), pilot tones & dataless channel, binary offset carrier (BOC) modulation, and so on. The last section is conclusion, in which the whole paper will be retrospected and some useful conclusion will be given.

To determine whether logarithmic and polynomial models are superior to simple linear models for predicting reference values for M-mode echocardiographic variables in dogs with a wide range of body weights. 69 apparently healthy adult male and female dogs of various breeds, ages (range, 1 to 12 years; median, 3.5 years), and body weights (range, 3.9 to 977 kg; median, 25.4 kg). Echocardiographic M-mode measurements of the interventricular septum, left ventricular dimension (LVD), left ventricular wall, aorta, and left atrium were obtained. Simple linear, second-order polynomial, third-order polynomial, and logarithmic regression models were determined by use of the least-squares method to describe the relationship between M-mode measurements and body weight. Differences in adjusted R2 values of logarithmic and polynomial models were tested for significance of contribution, compared with the simple linear model. Significant differences in adjusted R2 were found when comparing simple linear with logarithmic or polynomial models for LVD-diastole, LVD-systole, aorta, and left atrium. Differences in adjusted R2 between second-order polynomial, third-order polynomial, and logarithmic models were not significant for any M-mode measurement. In this study, logarithmic or second-order polynomial models predicted reference values of M-mode measurements for size of the cardiac chambers better than simple linear models for dogs with a wide range of body weights. Logarithmic and polynomial models were not superior to simple linear models for M-mode measurements of cardiac wall thickness.

Global Positioning System (GPS) radio occultation signals received by a low Earth orbit (LEO) satellite provide information about the global distribution of electron density in the ionosphere. We examine two radio occultation inversion algorithms. The first algorithm utilizes the Abel integral transform, which assumes spherical symmetry of the electron density field. We test this algorithm with two approaches: through the computation of bending angles and through the computation of total electron content (TEC) assuming straight line propagation. We demonstrate that for GPS frequencies and for observations in LEO, the assumption of straight‐line propagation (neglecting bending) introduces small errors when monitoring the F2 layer. The second algorithm, which also assumes straight‐line propagation, is a three‐dimensional (3‐D) inversion constrained with the horizontal structure of a priori electron density fields. As a priori fields we use tomographic solutions and the parameterized real‐time ionospheric specification model (PRISM) when adjusted with ionosonde data or ground‐based GPS vertical TEC maps. For both algorithms we calibrate the occultation data by utilizing observations from the part of the LEO that is closer to the GPS satellite. For inversions we use dual‐frequency observational data (the difference of Ll and L2 phase observables) which cancel orbit errors (without applying precise orbit determination) and clock errors (without requiring synchronous ground data) and thus may allow inversions to be computed close to real time in the future. The Abel and 3‐D constrained algorithms are validated by statistically comparing 4 days of inversions with critical frequency (ƒ0F2) data from a network of 45 ionosonde stations and with vertical TEC data from the global network of GPS ground receivers. Globally, the Abel inversion approach agrees with the ƒ0F2 correlative data at the 13% rms level, with a negligible mean difference. All tested 3‐D constrained inversion approaches possess a statistically significant mean difference when compared with the ionosonde data. The vertical TEC correlative comparisons for both the Abel and 3‐D constrained inversions are significantly biased (∼30%) by the electrons above the 735‐km LEO altitude.

Comparison and analysis of unmodelled errors in GPS and BeiDou signals

Land vehicle positioning relies mostly on satellite navigation systems such as the Global Positioning System (GPS). However, GPS signals may be degraded or suffer from blockage in urban canyons and tunnels, and the positioning information provided is interrupted. One solution for such a problem is to integrate GPS with an inertial measurement unit (IMU) and the navigation solution is achieved using an estimation technique which is traditionally based on a Kalman filter (KF). In order to have a low cost navigation solution for land vehicles, MEMS-based inertial sensors are used. To further reduce the cost a reduced inertial sensor system (RISS) which consists of only one gyroscope and a speed sensor is integrated with GPS. The position and velocity errors can be estimated by a KF relying on RISS dynamic error model and GPS position and velocity updates. However, low-cost MEMS sensors suffer from complex error characteristics, which are difficult to model by the linearized KF models. The positional accuracy of the integrated system can be improved using Parallel Cascade Identification (PCI) that is cascaded with the KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear residual RISS errors are modeled by PCI. The performance of this method is examined by road test trajectories in a land vehicle and compared to KF.

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.

The Global Positioning System (GPS) is a satellite navigation system and positioning that is owned and managed by the United States of America. This system is also designed to provide three-dimensional position and speed and information about time, continuously throughout the world without depending on time and weather, to many people simultaneously. The GPS module undergoes system expansion on low-cost devices by producing low-cost and energy-saving devices, including being able to provide L1 satellite data equivalent to geodetic GPS. This study shows whether low-cost U-Blox GPS can be equivalent to Hi-Target V60 geodetic GPS. The test was carried out by observing the four corners of the Geomatics Engineering volleyball field and PWK ITS using the RTK method. The results of GPS observations were carried out statistical tests to see if low-cost U-Blox GPS can be equivalent to GPS Geodetic Hi-Target V60.

BackgroundPatient‐based real‐time quality control (PBRTQC) is a valuable tool for monitoring the performance of testing processes. We aimed to compare and optimize various PBRTQC procedures for serum sodium.MethodsIn a computer simulation, artificial errors were added to 680,000 real patients’ results. The characteristics of error detection of various algorithms—moving average, moving median, moving SD and moving proportion of normal results including different control limits (CLs)—were assessed on their ability to detect critical errors early.ResultsThe moving average and moving median were sensitive to system error, and the moving SD tended to detect random error. P3SD (moving proportion of normal results, CLs based on mean and SD of proportion of normal results) demonstrated excellent performance for both system error and random error. The increase of block sizes (N) leads to the delay of error detection and the decrease of false rejection, except for QC procedures with minimum and maximum as CLs. CLs calculation with “0.1% false alarm rate” had more effective performance than that set false alarm to zero (minimum and maximum as CLs). The impact of truncation on QC performance depended on truncation limits, algorithms and the types of error. The significant improvement in QC performance due to truncation was only found in moving SD.Conclusion“P3SD,N = 50, without truncation” and “moving SD, N = 25, set 0.1% false alarm as CLs and set 1% outliers exclusion as truncation limits” were recommended as the optimized procedures for serum sodium to monitor system error and random error, respectively.

The Georgia Institute of Technology is evaluating the feasibility and effectiveness of mileage-based pricing programs as transportation control measures. Incentives were provided to study participants who change driving behavior in response to cent per mile pricing (fixed pricing and pricing as a function of congestion level). In-vehicle Global Positioning System (GPS) devices were used to estimate distance traveled and driver behavior (e.g., speed and acceleration profiles). The accuracy of estimated mileage accrual speeds by road classification, and acceleration rates used in pricing algorithms, is paramount. Various data-smoothing techniques were applied to the instrumented vehicle GPS speed data, and performance of the algorithms was evaluated in minimizing the impact of GPS random error on speed, acceleration, and distance estimates. The conventional discrete Kalman filter algorithm was modified to enhance its ability to control GPS random errors. Each smoothing method produces different second-by-se...

In the geodetic data processing field, most methods for dealing with inequality constraints model are based on additive random error (ARE) models, and there have been few studies on mixed additive and multiplicative random error (MAAMRE) models with inequality constraints. To address this problem, a MAAMRE model with inequality constraints is first established based on the definition of inequality constraint equations, and then, a corresponding parameter estimation algorithm is proposed based on the idea of an exhaustive search method. In addition, considering a MAAMRE model for an ill-posed problem, an iterative regularization solution for an ill-posed MAAMRE model is first derived, and then, a specific parameter estimation algorithm for an ill-posed MAAMRE model with inequality constraints is further proposed by applying the exhaustive search approach. Finally, the feasibility and advantages of the proposed algorithms are verified by global positioning system (GPS) elevation fitting model and digital terrain model (DTM) examples.Graphical

Although a USV navigation system is an important application of unmanned systems, combining Inertial Navigation System (INS) with Global Positioning System (GPS) can provide reliable and continuous solutions of positioning and navigation based on its several advantages; the random error characteristics of INS and the instability derived from the GPS signal blockage represent a potential threat to the INS/GPS integration of USV. Under this background, a composition framework based on nonlinear generalization capability of support vector machines (SVM) and multi-resolution ability of wavelet transform is used to solve the difficulty that the INS suffers from the interference of stochastic errors, and the dynamic information of the USV is not influenced. An innovative fuzzy broad learning structure based on the broad learning (BL) method is utilized in the INS/GPS integration of USV, in which the navigation information of INS and GPS are deemed as the input of the Fuzzy Broad Learning System (FBLS) to train the network, and then the trained network of FBLS and navigation information of INS are applied for estimating the optimal navigation solution during the GPS signal blockage. Based on the USV platform, a sea trial was carried out to confirm the validity and feasibility of the proposed method by comparing with existing algorithms for INS/GPS integration. The experimental results show that the proposed approach could achieve the better denoising effect from random errors of INS and provide high-accuracy navigation solutions during GPS signal blockage.

Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%.

The Global Positioning System (GPS) is a satellite navigation system currently under development by the Department of Defense. A recent note described the numerical properties of a proposed navigation algorithm for the low–cost GPS receiver under static conditions. This note evaluates performance of this algorithm in a realistic dynamic simulation of a typical flight profile for an Air Force C5A transport aircraft. Numerical convergence and long–term dynamic stability of the algorithm are demonstrated in a noise–free environment; typically, a single iteration of the algorithm maintains position estimate errors below 0.2 ft at each position fix for the duration of the flight. Stochastic error models are implemented in the simulation to produce a realistic noisy environment; in this case, the algorithm provides navigation accuracies on the order of 400 ft. In addition, the performance of two GPS satellite selection routines is compared in the simulations.