Online Calibration Method Research Articles

An online recursive autocalibration of triaxial accelerometer.

In this paper, we proposed a novel method for autocalibration of triaxial Micro-Electro-Mechanical systems (MEMS) accelerometer that does not require any sophisticated laboratory facilities. In particular, this method is an online calibration method which can be conveniently implemented with the accuracy of MEMS accelerometer being significantly improved. The procedure exploits the fact that the output vector of the accelerometer must match the local gravity in static state condition. To achieve online calibration, the model as well as the cost function are linearized at the beginning, and an online recursive method is then utilized to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is based on damped recursive least square estimation (DRLS), which can significantly reduce the calculation complexity comparing to nonlinear optimization method. In addition, the unknown parameters can be solved in a short time and the estimated parameters can remain stable during calibration. Experimentally, this method was tested by comparing the output results before and after calibration in different condition. It showed that the output, after calibrated by the proposed method, is more accurate with respect to raw output using default factory parameters.

HYBRID ONLINE MOBILE LASER SCANNER CALIBRATION THROUGH IMAGE ALIGNMENT BY MUTUAL INFORMATION

Abstract. This paper proposes an hybrid online calibration method for a laser scanner mounted on a mobile platform also equipped with an imaging system. The method relies on finding the calibration parameters that best align the acquired points cloud to the images. The quality of this intermodal alignment is measured by Mutual information between image luminance and points reflectance. The main advantage and motivation is ensuring pixel accurate alignment of images and point clouds acquired simultaneously, but it is also much more flexible than traditional laser calibration methods.

HYBRID ONLINE MOBILE LASER SCANNER CALIBRATION THROUGH IMAGE ALIGNMENT BY MUTUAL INFORMATION

This paper proposes an hybrid online calibration method for a laser scanner mounted on a mobile platform also equipped with an imaging system. The method relies on finding the calibration parameters that best align the acquired points cloud to the images. The quality of this intermodal alignment is measured by Mutual information between image luminance and points reflectance. The main advantage and motivation is ensuring pixel accurate alignment of images and point clouds acquired simultaneously, but it is also much more flexible than traditional laser calibration methods.

On-site calibration method for outdoor binocular stereo vision sensors

Using existing calibration methods for binocular stereo vision sensors (BSVS), it is very difficult to extract target characteristic points in outdoor environments under complex light conditions. To solve the problem, an online calibration method for BSVS based a double parallel cylindrical target and a line laser projector is proposed in this paper. The intrinsic parameters of two cameras are calibrated offline. Laser strips on the double parallel cylindrical target are mediated to calibrate the configuration parameters of BSVS. The proposed method only requires images of laser strips on the target and is suitable for the calibration of BSVS in outdoor environments. The effectiveness of the proposed method is validated through physical experiments.

A novel separation and calibration method for DVL and compass error in dead reckoning navigation systems

The scale factor error of the Doppler velocity log (DVL) and the heading angle error of a compass are so integrated in dead reckoning (DR) navigation systems that it is difficult to separate them. This paper aims to solve this problem by putting forward an online separation and calibration method for and based on an ‘arc and linear’ trajectory. This method introduces the high-accuracy location information of a long base line (LBL) acoustic positioning system. At first, the relationship between the displacements on the ‘arc’ trajectory in directions of east and north, output by the LBL and DR systems, serves to judge the carrier direction and calibrate . And then by compensating , the displacement on the ‘linear’ trajectory is used to calibrate . Finally, a semi-physical simulation experiment is conducted to test and verify this calibration method to see how effective and accurate it is. Experimental results show that after calibration the residual error ratios of and are 8.24% and 3.70% respectively. Therefore, online calibration of and is realized effectively. What’s more, when the DR system is working alone in 400 s, this method reduces position error by up to 93.39%, from 18.91 m to 1.25 m.

High Accuracy On-Line Calibration System for Current Transformers Based on Clamp-Shape Rogowski Coil and Improved Digital Integrator

To solve the problems of the existing on-site calibration methods, for instance, the methods need power off, and the standard transformers have large volume, heavy weight and small dynamic range, this paper proposes a high accuracy on-line calibration method for current transformers. By using a clamp-shape Rogowski coil as the standard current transformer, instead of traditional electromagnetic transformer, the volume and weight are reduced greatly, and the power of the line needn’t to be interrupted. The output signal of the clamp-shape Rogowski coil needs to be integrated, and to overcome the problems of analog integrator, which have temperature and zero drift, a high accuracy digital integrator is proposed in the paper to improve the accuracy and stability of the signal processing circuit. Test results indicate that the accuracy of the whole calibration system can reach up to 0.05 accuracy class. The on-line calibration system can calibrate the traditional and electronic current transformers when the line is energized.

Two new online calibration methods for computerized adaptive testing

Two new online calibration methods for computerized adaptive testing

A New Online Calibration Method for Multidimensional Computerized Adaptive Testing.

Multidimensional-Method A (M-Method A) has been proposed as an efficient and effective online calibration method for multidimensional computerized adaptive testing (MCAT) (Chen & Xin, Paper presented at the 78th Meeting of the Psychometric Society, Arnhem, The Netherlands, 2013). However, a key assumption of M-Method A is that it treats person parameter estimates as their true values, thus this method might yield erroneous item calibration when person parameter estimates contain non-ignorable measurement errors. To improve the performance of M-Method A, this paper proposes a new MCAT online calibration method, namely, the full functional MLE-M-Method A (FFMLE-M-Method A). This new method combines the full functional MLE (Jones & Jin in Psychometrika 59:59-75, 1994; Stefanski & Carroll in Annals of Statistics 13:1335-1351, 1985) with the original M-Method A in an effort to correct for the estimation error of ability vector that might otherwise adversely affect the precision of item calibration. Two correction schemes are also proposed when implementing the new method. A simulation study was conducted to show that the new method generated more accurate item parameter estimation than the original M-Method A in almost all conditions.

Robust Estimators for Variance-Based Device-Free Localization and Tracking

Device-free localization systems, such as variance-based radio tomographic imaging (VRTI), use received signal strength (RSS) variations caused by human motion in a static wireless network to locate and track people in the area of the network, even through walls. However, intrinsic motion, such as branches moving in the wind or rotating or vibrating machinery, also causes RSS variations which degrade the performance of a localization system. In this paper, we propose a new estimator, least squares variance-based radio tomography (LSVRT), which reduces the impact of the variations caused by intrinsic motion. We compare the novel method to subspace variance-based radio tomography (SubVRT) and VRTI. SubVRT also reduces intrinsic noise compared to VRTI, but LSVRT achieves better localization accuracy and does not require manually tuning additional parameters compared to VRTI. We also propose and test an online calibration method so that LSVRT and SubVRT do not require “empty-area” calibration and thus can be used in emergency situations. Experimental results from five data sets collected during three experimental deployments show that both estimators, using online calibration, can reduce localization root mean squared error by more than 40 percent compared to VRTI. In addition, the Kalman filter tracking results from both estimators have 97th percentile error of 1.3 m, a 60 percent reduction compared to VRTI.

Fast Three-Dimensional Single-Particle Tracking in Natural Brain Tissue

Observation of molecular dynamics is often biased by the optical very heterogeneous environment of cells and complex tissue. Here, we have designed an algorithm that facilitates molecular dynamic analyses within brain slices. We adjust fast astigmatism-based three-dimensional single-particle tracking techniques to depth-dependent optical aberrations induced by the refractive index mismatch so that they are applicable to complex samples. In contrast to existing techniques, our online calibration method determines the aberration directly from the acquired two-dimensional image stream by exploiting the inherent particle movement and the redundancy introduced by the astigmatism. The method improves the positioning by reducing the systematic errors introduced by the aberrations, and allows correct derivation of the cellular morphology and molecular diffusion parameters in three dimensions independently of the imaging depth. No additional experimental effort for the user is required. Our method will be useful for many imaging configurations, which allow imaging in deep cellular structures.

An on-line calibration method for camera with large FOV based on prior information

In the large dimensional vision measurement, the parameters of camera with large FOV (field-of-view) are easy to change for the influence of working conditions, which directly affects the precision of measurement. Concerning this issue, an on-line calibration method for camera with large FOV based on prior information is proposed. In this method, sufficient prior information is firstly obtained by the lights whose directions and positions in the space are known before measurement, so as to achieve the high accuracy pre-calibration of camera with large FOV, in which the initial values of camera parameters are acquired. During the measurement, on-line calibration is conducted with several fixed points in the FOV by combining EPnP algorithm, a pose estimation algorithm based on lines, and LM (Levenberg–Marquardt) optimization algorithm, which adjusts the parameters of camera in time. The experiments results show that the precision of the on-line calibration method is equal to that of the off-line traditional calibration methods, whose reconstruction error is within 2mm with the FOV of 3000mm×4000mm, while the method is less time consuming. The method in this paper is proved suitable for the on-line calibration of camera with large FOV.

Online Calibration of Glucose Sensors From the Measured Current by a Time-Varying Calibration Function and Bayesian Priors.

Minimally invasive continuous glucose monitoring (CGM) sensors measure in the subcutis a current signal, which is converted into interstitial glucose (IG) concentration by a calibration process periodically updated using fingerstick blood glucose (BG) references. Though important in diabetes management, CGM sensors still suffer from accuracy problems. Here, we propose a new online calibration method improving accuracy of CGM glucose profiles with respect to manufacturer calibration. The proposed method fits CGM current signal against the BG references collected twice a day for calibration purposes, by a time-varying calibration function whose parameters are identified in a Bayesian framework using a priori second-order statistical knowledge. The distortion introduced by BG-to-IG kinetics is compensated before parameter identification via nonparametric deconvolution. The method was tested on a database where 108 CGM signals were collected for 7days by the Dexcom G4 Platinum sensor. Results show the new method drives to a statistically significant accuracy improvement as measured by three commonly used metrics: mean absolute relative difference reduced from 12.73% to 11.47%; percentage of accurate glucose estimates increased from 82.00% to 89.19%; and percentage of values falling in the "A" zone of the Clark error grid increased from 82.22% to 88.86%. The new calibration method significantly improves CGM glucose profiles accuracy with respect to manufacturer calibration. The proposed algorithm provides a real-time improvement of CGM accuracy, which can be crucial in several CGM-based applications, including the artificial pancreas, thus providing a potential great impact in the diabetes technology research community.

Integrated Method for Online Calibration of Real-Time Traffic Network Management Systems

This paper presents an integrated method for online calibration of realtime traffic network simulation models. The method integrates a time-dependent demand adjustment module and a link-based traffic flow propagation model calibration module. These modules use available realtime traffic observations to minimize inconsistency between the model estimation results and real-world observations. The modules are integrated into a real-time traffic network management system that was developed for the US-75 corridor in Dallas, Texas. Results illustrate that the online calibration method is effective in enhancing the model's consistency in the different operational conditions.

基于激光传感的料堆体积测量在线标定方法

基于激光传感的料堆体积测量在线标定方法

Online camera-gyroscope autocalibration for cell phones.

The gyroscope is playing a key role in helping estimate 3D camera rotation for various vision applications on cell phones, including video stabilization and feature tracking. Successful fusion of gyroscope and camera data requires that the camera, gyroscope, and their relative pose to be calibrated. In addition, the timestamps of gyroscope readings and video frames are usually not well synchronized. Previous paper performed camera-gyroscope calibration and synchronization offline after the entire video sequence has been captured with restrictions on the camera motion, which is unnecessarily restrictive for everyday users to run apps that directly use the gyroscope. In this paper, we propose an online method that estimates all the necessary parameters, whereas a user is capturing video. Our contributions are: 1) simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter and 2) generalization of the multiple-view coplanarity constraint on camera rotation in a rolling shutter camera model for cell phones. The proposed method is able to estimate the needed calibration and synchronization parameters online with all kinds of camera motion and can be embedded in gyro-aided applications, such as video stabilization and feature tracking. Both Monte Carlo simulation and cell phone experiments show that the proposed online calibration and synchronization method converge fast to the ground truth values.

Magnetometer Calibration Using Kalman Filter Covariance Matrix for Online Estimation of Magnetic Field Orientation

The inertial/magnetic measurement units are an affordable instrument for the determination of orientation. The sensors embedded in the system are affected by nonidealities that can be greatly compensated by proper calibration, by determining sensor parameters, such as bias, misalignment, and sensitivity/gain. This paper presents an online calibration method for a three-axial magnetometer using a 3-D Helmholtz coil. The magnetometer is exposed to different directions of the magnetic field created by the 3-D coil. The parameters are estimated by using an unscented Kalman filter. The directions are calculated online by using a sensor parameter covariance matrix. The method evaluation is achieved by first running numerous simulations, followed by experiments using a real magnetometer, finally resulting in better accuracy of parameter estimation with a low number of measurement iterations compared with the method where magnetic field directions are determined manually.

An 8-Bit 1.72-Gsample/s Two Channel TimeInterleaved Analog-to-Digital Converter Based on PCB Circuit Board

This paper reports an 8-bit 1.72-Gsample/s TimeInterleaved analog-to-digital converter (TIADC) based on PCB with Field Programmable Gate Array (FPGA) technique. The system integrates two independent designed 8-bit 0.9-Gsample/s ADC chips in parallel, commercial FPGA and multi phase clock distributor circuit. In order to increase the systems performance, online calibration method is proposed to calibrate the mismatching errors in TIADC. The utilization of the FPGA is proven to be effective in removing the offset & gain mismatch; the clock distributor circuit is used as the time delay for each Sub-ADC chip to eliminate the sampling-time error. The hardware design features are also described in details. The ADC chip is fabricated in 0.35 um SiGe BiCMOS. Finally, experimental results reveal that the proposed system is capable to be operated up to 1.72GSps.Under this sampling frequency (1.72GHz), the system can achieve spurious free dynamic range (SFDR) which is larger than 40dBc. The effective code (ENOB) is lager than 5.5 bit from DC to Nyquist frequency and the power dissipation is 2.28 W.

MSAHRS Online Calibration Method Aided by Star Sensors

In order to maintain the stability of precision of marine SAHRS, calibration should be made periodically. The paper proposed an online calibration method aided by star sensor.with the reference frame of boat frame on the beginning of calibration. By using the inertial attitude provided by star sensor as the attitude reference, the gyroscope constant drift can be estimated and attenuated. The difference of the integral of special force and projection of gravity on inertial space taken as the measurements,the zero bias of accelerometer can be calibrated.The method need not the disassembly of system,which is well suitble for online calibration.The computer simulation verify that the method can identify all the constant errors of SAHRS online not only in moorage but also on rocking base.

On-line Calibration of Semiconductor Gas Sensors Based on Prediction Model

In this study, an Electronic nose (Enose) instrument used indoor for monitoring formaldehyde is designed. In mass production of this instrument, because of the inherent variability in the sensor manufacturing process, the Enose instruments give different outputs. It is impossible to train an individual prediction model on each instrument to have uniform output. A new on-line calibration method based on prediction model without real master instrument is proposed. This method avoids the problem that if the real master instrument behaves drift, the calibration of the other batch of instruments would lose its effect. In this paper, the prediction model is radial basis function (RBF) neural network and particle swarm optimization (PSO) is used to determine the parameters in RBF. The results show that the responses of the same type sensors are uniform after calibration, and this new method is easy and robust.

An Online Calibration Method for Manipulator with Joint Clearance

An Online Calibration Method for Manipulator with Joint Clearance