Compensation Of Bias Research Articles

Evaluation and refinement of ERA5-land 2 m atmospheric temperature in GNSS precipitable water vapor

Since the land 2 m air temperature from European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5-land) is vulnerable to interference by regional climate characteristics and local topography, how to objectively and accurately evaluate its accuracy and then establish a refined model is the key to improve its multifaceted and deep-level applications. This study proposed a systematic and complete refinement method for ERA5-land air temperature based on the densely distributed weather stations in Shandong Province in eastern China, including the compensation of elevation matching bias (EMB) caused by ASTER GDEM and the overall refinement using the remove-and-restore model (RRM). Taking the hottest July and the coldest December in this area, surface air temperature, atmospheric weighted mean temperature (Tm) and GNSS precipitable water vapor (PWV) were used to verify the feasibility of this method. The results show that: (1) Whether the temperature is high in July or low in December, the accuracy can be improved by about 42.2 % when the refinement method was used. Especially for high altitude areas (the summit of Mount Tai, 1553 m above sea level), the accuracy can be improved by 74.5 %. (2) In contrast to only the temperature lapse rate (TLR) correction, the average root mean square (RMS) can be reduced by 7.4 % and 60.4 % in July and 7.0 % and 59.5 % in December for the met stations less than 150 m in altitude and the peak station of Mount Tai, respectively, when the new method was used. (3) Compared with the Tm and PWV computed by interpolated temperature without any correction, the accuracy of Tm can be improved by 31.2 % and 34.1 % in July and December for weather station in the altitude of 100 ∼ 350 m after the TLR correction, EMB compensation and RRM refinement successively. Meanwhile, the accuracy of PWV can be improved by 47.8 % in July and 42.0 % in December, respectively, and especially for PWV high-value moments. Besides, after correction by RRM, both Tm and PWV in low altitude areas achieve at least 20 % accuracy improvement. In summary, the systematic and complete refinement method proposed in this study are important to significantly improve the ERA5-land 2 m temperature availability and GNSS PWV accuracy, and the compensation of EMB is mainly effective for high altitude areas.

Theoretical Relationship Among Effective Lens Position, Predicted Refraction, and Corneal and Intraocular Lens Power in a Pseudophakic Eye Model.

PurposeTo ascertain the theoretical impact of anatomical variations in the effective lens position (ELP) of the intraocular lens (IOL) in a thick lens eye model. The impact of optimization of IOL power formulas based on a single lens constant was also simulated.MethodsA schematic eye model was designed and manipulated to reflect changes in the ELP while keeping the optical design of the IOL unchanged. Corresponding relationships among variations in ELP, postoperative spherical equivalent refraction, and required IOL power adjustment to attain target refractions were computed for differing corneal powers (38 diopters [D], 43 D, and 48 D) with IOL power ranging from 1 to 35 D.ResultsThe change in ELP required to compensate for various systematic biases increased dramatically with low-power IOLs (less than 10 D) and was proportional to the magnitude of the change in refraction. The theoretical impact of the variation in ELP on postoperative refraction was nonlinear and highly dependent on the optical power of the IOL. The concomitant variations in IOL power and refraction at the spectacle plane, induced by varying the ELP, were linearly related. The influence of the corneal power was minimal.ConclusionsThe consequences of variations in the lens constant mainly concern eyes receiving high-power IOLs. The compensation of a systematic bias by a constant increment of the ELP may induce a nonsystematic modification of the predicted IOL power, according to the biometric characteristics of the eyes studied.Translational RelevanceOptimizing IOL power formulas by altering the ELP may induce nonsystematic modification of the predicted IOL power.

Decorrelated Debiased Converted Measurement Kalman Filter for Phased Array Radar Tracking System

In this article, the target tracking of phased array radars with measurements specified in the direction cosine coordinate system is considered. The converted measurement Kalman filter is a widely adopted technique since it allows tracking in the coordinate system in which the dynamics of targets are described. The converted measurement Kalman filter has two types of inherent bias: conversion and estimation biases. The compensation of conversion bias has been examined by earlier studies using the debiasing technique in the presence of estimation bias. This article, therefore, introduces decorrelated debiased converted measurement Kalman filter (DDCMKF) in the direction cosine coordinate system to relieve the estimation bias. The decorrelation between the Kalman gain and measurement residual by applying conditioning on predicted position estimates is first introduced. The range-rate measurement is subsequently incorporated as a pseudo-measurement form after the derivation of decorrelated converted position. Finally, a sequential DDCMKF in direction cosine coordinates is developed using the derived converted position and range-rate measurement models. Adopting sequential filtering for incorporating the range-rate measurement reduces computation complexity and alleviates its strong nonlinearity. Numerical simulations are conducted to evaluate the performance of DDCMKF against other types of tracking filters in the direction cosine coordinate system. Simulation results show that the proposed DDCMKF achieves preferable error performance while maintaining consistency, especially in short-range tracking with relatively large direction cosine errors.

Evaluation of Applied Force During Nasopharyngeal Swab Sampling Using Handheld Sensorized Instrument.

Nasopharyngeal swab is the most widely used diagnostic test for COVID-19 detection. However, enormous tests have posed a high risk of infection to medical professionals due to close contact with patients and substantial health burden. While automation of the nasopharyngeal swab is regarded as a potential solution to address these problems, the quantitative study of force for safe and effective control has not been widely performed yet. Hence, this study presents applied force during the standard nasopharyngeal swab sampling procedure using a handheld sensorized instrument. The sensorized instrument can simultaneously measure multi-axis forces and 6-DOF hand motion while allowing natural hand motion as is used in the standard swab sampling. To accurately measure force from the handheld instrument, the compensation of gravity bias is accomplished online while estimating the orientation of the hand with an embedded IMU sensor. As a result, the instrument can measure all three-axes forces by an error below 5 mN. A simulated test on a phantom model using the sensorized instrument shows that how the forces vary during the sampling sequences.

Effect of Random Error of Temperature Sensors on the Quality of Temperature Compensation of FOG Bias by a Neural Network

Effect of Random Error of Temperature Sensors on the Quality of Temperature Compensation of FOG Bias by a Neural Network

Modeling and Compensation of the Penetration Bias in InSAR DEMs of Ice Sheets at Different Frequencies

Interferometric synthetic aperture radar (InSAR) is able to provide important information for the characterization of the surface topography of glaciers and ice sheets. However, due to the inherent penetration of microwaves into dry snow, firn, and ice, InSAR elevation models are affected by a penetration bias. The fact that this bias depends on the snow and ice conditions as well as on the interferometric acquisition parameters complicates its assessment and makes it also relevant for measuring topographic changes. Recent studies indicated the potential for model-based compensation of this penetration bias. This article follows this approach and investigates the performance of two subsurface volume models for this task. Single-channel and polarimetric approaches are discussed for random and oriented volume scenarios. The model performance is assessed on two test sites in the percolation zone of the Greenland ice sheet using fully polarimetric airborne X-, C-, L-, and P-band InSAR data. The results indicate that simple models can partially compensate the penetration bias and provide more accurate topographic information than the interferometric phase center measurements alone.

Dual-loop Sagnac interferometer with a geometric phase shifter for quadrature phase bias locking.

A stable quadrature phase bias is highly demanded in the balanced Sagnac interferometer to achieve sufficient detection sensitivity and unambiguity. We demonstrate a straightforward and effective quadrature phase locking technique in a free-space balanced Sagnac interferometer by using a dual-loop, one for sensing and the other for bias feedback. The sensing loop and the feedback loop operate on linearly polarized beams at two separate wavelengths and overlap for most of the area. A geometric phase shifter showing wavelength independence placed on the common path of the two loops introduces two almost identical nonreciprocal phase shifts between the counterpropagating beams for the two separate wavelengths, so that real-time compensation of the phase bias for the sensing loop can be implemented by the error signal of the feedback loop. Proof-of-concept experimental results demonstrated successful locking of the quadrature phase bias in the presence of signal fading due to the birefringence disturbance. The correction of the residual chromatism of the interferometer is discussed before the conclusion is made.

Compensation and Amplification of Attenuation Bias in Causal Effect Estimates.

Covariate-adjusted treatment effects are commonly estimated in non-randomized studies. It has been shown that measurement error in covariates can bias treatment effect estimates when not appropriately accounted for. So far, these delineations primarily assumed a true data generating model that included just one single covariate. It is, however, more plausible that the true model consists of more than one covariate. We evaluate when a further covariate may reduce bias due to measurement error in another covariate and in which cases it is not recommended to include a further covariate. We analytically derive the amount of bias related to the fallible covariate's reliability and systematically disentangle bias compensation and amplification due to an additional covariate. With a fallible covariate, it is not always beneficial to include an additional covariate for adjustment, as the additional covariate can extensively increase the bias. The mechanisms for an increased bias due to an additional covariate can be complex, even in a simple setting of just two covariates. A high reliability of the fallible covariate or a high correlation between the covariates cannot in general prevent from substantial bias. We show distorting effects of a fallible covariate in an empirical example and discuss adjustment for latent covariates as a possible solution.

An Innovative Strategy for Accurate Thermal Compensation of Gyro Bias in Inertial Units by Exploiting a Novel Augmented Kalman Filter.

This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.

Compensation of temporal averaging bias in solar irradiance data

Solar irradiance data is used for the prediction of solar energy system performance but is presently a significant source of uncertainty in energy yield estimation. This also directly affects the expected revenue, so the irradiance uncertainty contributes to project risk and therefore the cost of finance. In this paper, the combined impact of temporal averaging, component deconstruction and plane translation mechanisms on uncertainty is analysed. A new method to redistribute (industry standard) hourly averaged data is proposed. This clearness index redistribution method is based on the statistical redistribution of clearness index values and largely corrects the bias error introduced by temporal averaging. Parameters for the redistribution model were derived using irradiance data measured at high temporal resolution by CREST, Loughborough University, over a 5 year period. The root mean square error (RMSE) of example net annual (2014) diffuse, beam and global yield of hourly averaged data were reduced from approximately 15% to 1%, 14% to 3% and 4% to 1%, respectively.

Molecular and Functional Bases of Selection against a Mutation Bias in an RNA Virus

The selective pressures acting on viruses that replicate under enhanced mutation rates are largely unknown. Here, we describe resistance of foot-and-mouth disease virus to the mutagen 5-fluorouracil (FU) through a single polymerase substitution that prevents an excess of A to G and U to C transitions evoked by FU on the wild-type foot-and-mouth disease virus, while maintaining the same level of mutant spectrum complexity. The polymerase substitution inflicts upon the virus a fitness loss during replication in absence of FU but confers a fitness gain in presence of FU. The compensation of mutational bias was documented by in vitro nucleotide incorporation assays, and it was associated with structural modifications at the N-terminal region and motif B of the viral polymerase. Predictions of the effect of mutations that increase the frequency of G and C in the viral genome and encoded polymerase suggest multiple points in the virus life cycle where the mutational bias in favor of G and C may be detrimental. Application of predictive algorithms suggests adverse effects of the FU-directed mutational bias on protein stability. The results reinforce modulation of nucleotide incorporation as a lethal mutagenesis-escape mechanism (that permits eluding virus extinction despite replication in the presence of a mutagenic agent) and suggest that mutational bias can be a target of selection during virus replication.

Improving the accuracy of derivation of the Williams’ series parameters under mixed (I+II) mode loading by compensation of measurement bias in the stress field components data

A new method for compensation of bias in the stress field components measurement data used for Williams’ series parameters derivation was presented. Essential increase of accuracy of derivation of SIF-related leading terms in series under mixed (I+II) mode loading was demonstrated. It was shown that a relatively low value of bias in the stress field components data error could result in the essential deviation of the values of derived Williams’ coefficients and the crack tip coordinates.

Research of Comprehensive Compensation of Harmonics, Reactive current and DC Bias in UHV Power Grid

A comprehensive compensation device with paralleled decoupling for suppressing harmonics current, reactive power, and DC Bias of power transformer is proposed. Harmonics current and reactive current are detected by instantaneous reactive power theory. In this way, the fundamental active power is converted to be compensated reactive power and harmonics by controlling the converter. Compared to the ASVG with IGBT, the capacity and withstanding voltage level of ASVG with GTO are improved. The voltage across the devices of APF with multi-level inverter is reduced because of the paralleled decoupling connection. The harmonics compensation with multi-stage inverter can also reduce the voltage across the devices effectively. The reactive power compensator can work as a controllable rectifier and output controllable current by changing control algorithm. This current injects neutral point of transformer and suppresses its DC Bias, which has obvious effect on a power transformer. As a part of intelligent power grid, this compensator plays an important role for the development of smart grid automation.

Adaptive Redundant Inertial Measurement Unit/Global Positioning System Integration Filter Structure for Fault Tolerant Navigation

This paper proposes a new adaptive filter structure containing two subfilters, a fault detection and isolation (FDI) filter, and a main-filter for redundant inertial measurement unit (RIMU)/global positioning system (GPS) integrated navigation system tolerant toward faults of inertial sensors. The purpose of subfilters is compensation of sensor level bias (SLB) of the RIMU during in-flight alignment for successful FDI processing. Also, that of the main-filter is providing of error compensated navigation solution based on the good FDI result. To achieve these purposes, two coordinate frames, sub-body frame (SBF) and master-body frame (MBF), are defined first. Then, two different error models for the RIMU are formulated in the SBF for sensor level compensation and in the MBF for module level compensation, respectively. Based on the formulated error models, an adaptive filter structure is designed. Some numerical simulations are performed to validate the performance of the proposed adaptive filter structure.

Recursive Algorithms for Bias and Gain Nonuniformity Correction in Infrared Videos

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity.

Application of Least Squares-Support Vector Machine in system-level temperature compensation of ring laser gyroscope

The bias of Ring Laser Gyroscope (RLG) exhibits a non-ignorable characteristic of drift when its temperature changes, which is an important factor of reducing the navigation precision in inertial navigation systems. The limitations of least-squares fitting and neural network are pointed out and a new scheme of system-level temperature compensation of RLG bias based on Least Squares-Support Vector Machine (LS-SVM) is proposed. Temperature experiment of RLG bias is designed and carried out to validate the effectiveness of the proposed method. The traditional modeling method of least-squares fitting is also investigated to provide a comparison with the LS-SVM based method. Allan variance is used to analyze the error terms of RLG before and after compensation. The results show that: the bias instability of RLG output after it has been compensated by LS-SVM model is 1.32 × 10 −3 °/h, which has decreased 8.57 × 10 −3 °/h from that before compensation. It indicates that this method has reduced the influence of temperature variation on the RLG bias effectively and improved the gyro’s accuracy.

INS/GPS Aided by Frequency Contents of Vector Observations With Application to Autonomous Surface Crafts

This paper presents a high-accuracy, multirate inertial navigation system (INS) integrating global positioning system (GPS) measurements and advanced vector aiding techniques for precise position and attitude estimation of autonomous surface crafts (ASCs). Designed to be implemented and tested in the DELFIMx catamaran developed at ISR/IST, the navigation system comprises an advanced inertial integration algorithm to account for coning and sculling motions, combined with an extended Kalman filter (EKF) for inertial sensor error compensation. Aiding gravitational observations are optimally exploited in the EKF, by deriving a sensor integration technique that takes into account the vehicle's dynamics bandwidth information to properly trace measurement disturbances and extract the relevant sensor information. The proposed aiding technique and the performance of the navigation system are assessed using experimental data obtained at seatrials with a low-cost hardware architecture installed on-board the DELFIMx platform. It is shown that the low frequency information embodied in pendular measurements improves the compensation of inertial sensor bias and noise, and consequently enhances the performance of position and attitude estimation. The overall improvements obtained with the vector aiding observations are also illustrated for the case of GPS signal outage, emphasizing the extended autonomy of the navigation system with respect to position aiding.

A non-linear observer for attitude estimation of a fixed-wing unmanned aerial vehicle without GPS measurements

In this paper we propose a simple non-linear observer for attitude estimation based only on output from a typical inertial measurement unit (IMU) and dynamic pressure sensor embarked on a low-cost unmanned aerial vehicle. In particular, we aim to provide a good quality attitude estimate in the absence of global positioning system (GPS) ground truth and with potential low-frequency bias and high-frequency noise in the IMU sensor measurements. In addition, the case where the IMU only provides gyrometer and accelerometer outputs is considered; that is, there is no magnetometer output or it cannot be used due to local magnetic disturbances such as are common on a vehicle with electric motors. The proposed observer uses a simple centripetal force model (based on gyrometer and dynamic pressure measurements), augmented by a first-order dynamic model for angle of attack, to estimate non-inertial components of the acceleration. This estimate is used to correct the accelerometer output to provide a low-frequency estimate of the gravitational direction. This inertial direction, along with the gyrometer output, is then used to drive a fully non-linear attitude observer posed on the orthogonal group of rotation matrices SO(3). The observer is augmented with an integral state that ensures compensation of gyrometer bias. The resulting observer is simple to implement and fully non-linear. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/inertial navigation system (INS) that was available for the data set.

Compensation of Bias in Lidar Wind Resource Assessment

A number of vector and volume averaging considerations arise in relation to remote sensing, and in particular, Lidar. 1) Remote sensing devices obtain vector averages. The magnitude of a vector average is less than or equal to the scalar average obtained over the same period. The use of Lidars in wind power applications has introduced practices entailing the estimation of scalar average point quantities by the measurement of vector averages over volumes and vice versa. The relationship between vector and scalar averages, and the relationship between volume and point measurements, must therefore both be understood. It is found that their ratio depends upon wind direction variability according to a Bessel function of the standard deviation of the wind direction during the averaging interval. The impact of wind direction variability on power production is also explored. 2) The finite probe length of remote sensing devices incurs a volume averaging bias if wind shear is non-linear. The sensitivity of the devices to signals from a range of heights produces volume averages representing wind speeds within that range. If the wind shear is described by a logarithmic wind profile the apparent height, at which the average wind speed occurs, is found to depend on simple geometrical arguments concerning configuration height and probe length independent of the degree of wind shear. Similar arguments are applied to determine the ideal height across the rotor at which to acquire wind speed data for power curves. 3) The common restriction of the locus of points at which radial velocity measurements are made to the circumference of a horizontally oriented disc at a particular height is seen to introduce ambiguity into results when dealing with wind vector fields which are not uniform.

Bias-compensation based method for errors-in-variables model identification

It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by noise. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compnesation based method is proposed for identification of noisy input-output system. The proposed method is based on compensation of asymptotic bias on the instrumental variables type (IV-type) estimates by making use of noise covariances estimates. In order to obtain the noise covariances estimates, an overdetermined system of equations is introduced, and the noise covariances estimation algorithm is derived by solving this overdetermined system of equations. From the combination of the parameter estimation algorithm and the noise covariances estimation algorithm, the proposed bias-compensated instrumental variables type (BCIV-type) method can be established. The results of a simulated example indicate that the proposed algorithm provides good estimates.