Self-calibration Method Research Articles

A miniature and intelligent Low-Power in situ wireless monitoring system for automotive wheel alignment

Automotive wheel misalignment is the most significant cause of excessive wear on tires, which will severely affect the stability and safety of vehicle handling, and cause serious consequences for human health and the environment. In this study, an energy-efficient onboard wheel alignment wireless monitoring system (WAWMS) is developed to detect wheel misalignment in real time. To minimise power consumption, a dual wake-up strategy is proposed to wake the microcontroller by a real-time clock (RTC) and an accelerometer. Furthermore, an online self-calibration method of inertial measurement unit (IMU) sampling frequency is investigated to improve measurement accuracy. Eventually, real-world wheel misalignment tests were performed with the WAWMS. The error-correcting output codes based support vector machines (ECOC-SVM) method successfully classifies different wheel alignment conditions with an average accuracy of 93.2% using nine principal components (PCs) of 3-axis acceleration spectrum matrixes. It validates the effectiveness of the designed WAWMS on automotive wheel alignment monitoring.

Self-Calibration Method of Sensors Array Errors Based on Rotation Measurement

Array calibration is the premise of successful array signal processing. The active calibration needs to know the direction of arrival (DOA) of calibration source signals accurately; otherwise, the performance will deteriorate. Although the conventional self-calibration does not need accurate DOA of calibration sources, it may suffer from falling into the local extremum since there are too many parameters to be estimated. Thus, we propose a novel self-calibration method based on the rotation measurement. The rotation measurement is that the array receives signals from a fixed source with different array rotation angles. The proposed method jointly estimates array errors and DOAs of the sources by an alternative minimization (AM) algorithm. Through the derivation of a suitable initial value in the AM algorithm, we could improve the robustness and reduce the computational complexity. The proposed method can achieve as high calibration accuracy as the active method even if there is an error in the source DOA information and can apply to arbitrary planar arrays. Both simulation and field experiment results demonstrate the effectiveness of the proposed method.

Self-Calibration Method for the Errors of Dual-Axis Turntable in Redundant Rotational Inertial Navigation System

Attitude measurement is an important capability of a redundant rotational inertial navigation system (RRINS). The attitude of RRINS is converted from the attitude of the redundant inertial measurement unit (RIMU) using a turntable. The attitude measurement accuracy of RRINS is reduced greatly by turntable errors because the turntable is rotated frequently when the RRINS is working. Therefore, it is important to calibrate the turntable errors for improving the attitude measurement accuracy of RRINS. In this study, a self-calibration method for the errors of a dual-axis turntable in RRINS is proposed. First, an error model of the dual-axis turntable is established, and the influence of turntable errors on RRINS attitude measurement is analyzed. Second, a self-calibration method, including rotation scheme, numerical algorithm (NA) errors calculation, and fruit fly optimization algorithm (FOA) errors estimation, is proposed. The influence of Earth’s rotational rate and gyroscope biases is eliminated during calibration using the designed rotation scheme, NA calculates the errors directly when they are minor angles, and FOA estimates the errors whether they are minor or large angles. The simulations and experiment results show that the proposed method can improve the attitude measurement accuracy of RRINS.

Honey–Water Content Analysis by Mixing Models Using a Self-Calibrating Microwave Method

Microwave techniques, as an indirect approach, can be applied for analyzing water content in honey by way of permittivity measurements. However, these techniques require proper calibration to accurately perform such indirect evaluation. Improper calibration standards used in this calibration process could naturally result in a reduction in the accuracy and thus the performance of dielectric characterization using microwaves. Self-calibrating microwave techniques can reduce the effects of imprecise standards and thus improve the performance of microwave measurements by bypassing the requirement of calibration standards. In this study, we develop a self-calibrating microwave measurement technique to determine the relative permittivity of honey samples and implement binary mixing models to predict adulteration levels of water-adulterated honey. From this implementation, it is observed that the parallel-capacitance mixing model could efficiently be applied to determine the concentration of water adulteration by examining the differences between absolute values of the real parts of the measured and predicted complex permittivities of adulterated honey.

A Novel Geometric Parameter Self-Calibration Method for Portable CBCT Systems

In outdoor environments or environments with space restrictions, it is difficult to transport and use conventional computed tomography (CT) systems. Therefore, there is an urgent need for rapid reconstruction of portable cone-beam CT (CBCT) systems. However, owing to its portability and the characteristics of temporary construction environments, high precision spatial location is difficult to achieve with portable CBCT systems. To overcome these limitations, we propose an iterative self-calibration improvement method with a self-calculated initial value based on the projection relationship and image features. The CT value of an open field image was used as the weight value of the projection data in the subsequent experiments to reduce the nonlinear attenuation of the projection intensity. Subsequently, an initial value was obtained based on the invariance of the rotation axis. Finally, self-calibration was realized iteratively using the reconstructed image. This method overcomes the main problem of the rotation axis invariance calibration algorithm—high similarity between the adjacent positions of symmetrical homogeneous materials. The proposed method not only improves the precision of self-calibration based on the projection relationship, but also reduces the performance cost and solution time of the self-calibration algorithm based on the image features. Thus, it satisfies the precision requirements for self-calibration of portable CBCT systems.

A Fast Continuous Self-Calibration Method for FOG Rotational Inertial Navigation System Based on Invariant Extended Kalman Filter

For a given device, the rapidity and accuracy of device error coefficient calibration and attitude alignment of the rotational inertial navigation system mainly depend on the convergence rate of the nonlinear filtering algorithm. The traditional extended Kalman filter method has no theoretical convergence guarantee for nonlinear systems and requires a long time in the actual calibration process. Since the attitude is subject to the 3-D rotation transformation group, the error transfer equation can be better linearized by defining the error on the lie group to use the new observation information fully. This article proposes a primary method of using the invariant extended Kalman filter to carry out an inertial navigation system. In the continuous rotation of the framework, the accelerometer, gyroscope, and framework angle are used to calibrate the error coefficient of the accelerometer and gyroscope and estimate the base alignment error angle. Simulation and experiment results show that the proposed method can accomplish alignment and calibration in 10 min under a large initial attitude error.

Development of an AD5933-based impedance calibration and measurement technology using piezoceramic transducers

AD5933 evaluation board is expected to promote the practicability of electromechanical impedance (EMI) based structural damage identification technology. For the evaluation board, the calibration before use is essential and important. In this study, a novel self-calibration method is proposed to help the board to improve its independence, measurement accuracy, and data continuity for the impedance measurement by selecting the optimal feedback resistance based on data deviation characteristics of the AD5933 board. In this research, to clearly present the principle of the proposed method, a pretest of impedance measurement via AD5933 board was conducted at first. Then, its impedance measurement performance was further investigated by a series of measurements tests, and the results demonstrated that the proposed method provide the board the ability to obtain the impedance measurement with higher accuracy and better data continuity comparing with the conventional subrange calibration method. At last, its feasibility and effectiveness for structural damage identification has also been validated by a bolt loosening identification test on a steel plate.

Martens hardness of Constantan thin films on (100) Si wafer: Improvement in contact area function in nanoindentation

This work presents a mechanical study by nanoindentation of Constantan thin films deposited by cathodic sputtering technique with an intermediate thin adhesion layer of titanium on a (100) silicon wafer substrate. A methodology based on a modified contact area function is proposed for a suitable processing of the nanoindentation data in order to extract the Martens hardness both of the substrate, titanium layer and Constantan films. The raw data of the substrate Martens hardness have been studied using the most useful models among them those of Nix & Gao, Li & Bradt and Bull & Page showing a significant indentation size effect. However, when considering the tip defect length in the Martens hardness computation, the corrected values are found constant and no indentation size effect occurs. Within this objective, an accurate determination of the tip defect length is required. Its value has been determined both with a correlation between the contact area function of Oliver & Pharr and the improved model of Chicot et al. and also with the self-calibration method proposed by Chicot et al. The tip defect length is afterwards implemented in the model of Jönsson & Hogmark modified by Rahmoun et al. for the hardness determination of the titanium layer and the Constantan thin films. As a main result, the Martens hardness of the titanium layer and of the substrate are found equal to 8 GPa thus allowing to neglect the influence of the titanium layer in the film hardness determination. As a main result, the hardness of the 4 Constantan films is constant whereas the substrate hardness changes with the film thickness. This unexpected behavior is related to the brittleness of the substrate where cracks are observed at the interface and by the film compacting which is trapped between the rigid indenter and the hard substrate.

Two-Step Self-Calibration of LiDAR-GPS/IMU Based on Hand-Eye Method

Multi-line LiDAR and GPS/IMU are widely used in autonomous driving and robotics, such as simultaneous localization and mapping (SLAM). Calibrating the extrinsic parameters of each sensor is a necessary condition for multi-sensor fusion. The calibration of each sensor directly affects the accurate positioning control and perception performance of the vehicle. Through the algorithm, accurate extrinsic parameters and a symmetric covariance matrix of extrinsic parameters can be obtained as a measure of the confidence of the extrinsic parameters. As for the calibration of LiDAR-GPS/IMU, many calibration methods require specific vehicle motion or manual calibration marking scenes to ensure good constraint of the problem, resulting in high costs and a low degree of automation. To solve this problem, we propose a new two-step self-calibration method, which includes extrinsic parameter initialization and refinement. The initialization part decouples the extrinsic parameters from the rotation and translation part, first calculating the reliable initial rotation through the rotation constraints, then calculating the initial translation after obtaining a reliable initial rotation, and eliminating the accumulated drift of LiDAR odometry by loop closure to complete the map construction. In the refinement part, the LiDAR odometry is obtained through scan-to-map registration and is tightly coupled with the IMU. The constraints of the absolute pose in the map refined the extrinsic parameters. Our method is validated in the simulation and real environments, and the results show that the proposed method has high accuracy and robustness.

Novel Self-Calibration Method for IMU Using Distributed Inertial Sensors

Inertial measurement unit (IMU) calibration is essential to ensure the successful operation of various navigation systems. This article describes a novel method for self-calibration of IMU with distributed sensor architectures. The IMU is composed of modules that are distributed along the measurement axes. Each module is equipped with a single-axis gyroscope and three-axis accelerometer sensors. The modules have also their own signal-conditioning circuits and processors. This enables single-axis calibration of the module with a servo system based on a piezoelectric actuator (PEA). The proposed IMU and calibration method are capable of on-site self-calibration. The calibration is performed with the sinusoidal movements generated by the servo system. Despite the limited displacement of the PEA, high angular velocity and tangential acceleration can be achieved with the servo system. Thus, the gyroscope sensor can be directly calibrated with the angular velocity produced by the servo system. On the other hand, the calibration parameters of the accelerometer are determined by using the tangential and centripetal acceleration produced by the servo system. With the proposed method, the bias, scale factor, and installation errors of accelerometer and gyroscope sensors can be determined. The method has been analyzed by conducting experiments with a prototype IMU. In addition, experiments on 3-DoF turntables have been conducted to confirm the effectiveness of the proposed self-calibration method.

Using angular two-point correlations to self-calibrate the photometric redshift distributions of DECaLS DR9

ABSTRACT Calibrating the redshift distributions of photometric galaxy samples is essential in weak lensing studies. The self-calibration method combines angular auto- and cross-correlations between galaxies in multiple photometric redshift (photo-z) bins to reconstruct the scattering rates matrix between redshift bins. In this paper, we test a recently proposed self-calibration algorithm using the DECaLS Data Release 9 and investigate to what extent the scattering rates are determined. We first mitigate the spurious angular correlations due to imaging systematics by a machine learning based method. We then improve the algorithm for χ2 minimization and error estimation. Finally, we solve for the scattering matrices, carry out a series of consistency tests, and find reasonable agreements: (1) finer photo-z bins return a high-resolution scattering matrix, and it is broadly consistent with the low-resolution matrix from wider bins; (2) the scattering matrix from the Northern Galactic Cap is almost identical to that from Southern Galactic Cap; (3) the scattering matrices are in reasonable agreement with those constructed from the power spectrum and the weighted spectroscopic subsample. We also evaluate the impact of cosmic magnification. Although it changes little the diagonal elements of the scattering matrix, it affects the off-diagonals significantly. The scattering matrix also shows some dependence on scale cut of input correlations, which may be related to a known numerical degeneracy between certain scattering pairs. This work demonstrates the feasibility of the self-calibration method in real data and provides a practical alternative to calibrate the redshift distributions of photometric samples.

Self-Calibration Method of Noncontact AC Voltage Measurement

Realizing stable and reliable monitoring of a distribution network voltage environment can obtain real-time power parameter information and ensure the normal and safe operation of transmission lines, which is of great research significance and engineering value. Based on the distributed capacitance relationship between sensor and transmission line, an equivalent circuit capacitance voltage dividing model is proposed, and the relevant factors affecting the stability of the voltage dividing ratio are analyzed. The self-calibration principle of noncontact AC voltage measurement is proposed based on the system identification theory. The noncontact sensing structure is designed, a sensor probe prototype is fabricated, and a back-end conditioning circuit is designed to realize the overall measurement system. Finally, the validity of the measurement model is verified by simulation and experiment, and a measurement platform is built which proves the feasibility of the self-calibration method for noncontact voltage measurement. The experimental results show that the error is less than ±2%. This method can correctly restore the measured voltage waveform, has good linearity, and can realize wireless data transmission, which provides a new idea for the voltage measurement method of a distribution network.

A Disturbance Estimation Approach to Self-Calibration of Gimbal Resolver- to-Digital Conversion System

High-precision position measurement of the gimbal servo system is indispensable to accurate torque output of the control moment gyroscope (CMG). This article addresses the systematic error of the resolver caused by the nonideal output signals of the resolver in the gimbal resolver-to-digital (R/D) conversion system. Due to the limitation of the structure and installation space of the CMG, it is impractical to install additional high-precision sensors to calibrate the raw angular position. Thus, this article proposes a new self-calibration method based on the discrete extended state observer (DESO) to compensate the systematic error of the resolver. The systematic error of the resolver is observed through iterating the resolver systematic estimation error and the compensation table is generated by the output of the DESO. Then, the steady-state error and dynamic performance of the DESO is analyzed and simulated. Finally, the proposed self-calibration method is verified by experiments, which show that the angular position control accuracy is significantly improved, which enhances the speed precision of the gimbal servo system.

Real-Time Self-Calibration Method for SO2 Ultraviolet Cameras

Real-Time Self-Calibration Method for SO2 Ultraviolet Cameras

A Self-Calibration SOC Estimation Method for Lithium-Ion Battery

Accurate state of charge (SOC) estimation is essential for the battery management system (BMS). In engineering, inappropriate selection of equivalent circuit model (ECM) and model parameters is common for lithium-ion batteries. It can result in systematic errors (i.e., modeling errors) in the state-space equation, thus affecting the SOC estimation accuracy. Aiming at that, this paper proposes a self-calibration method to enhance SOC estimation. In the method, a novel state-space equation containing an unknown systematic error term is developed based on the Thevenin model. A self-calibration unscented Kalman filter (SC-UKF) algorithm is then introduced for recursive SOC estimation. The algorithm can automatically recognize and calibrate the unknown systematic error in the state equation, while also reducing the random noise effect through data fusion with the measurement equation. Test results demonstrate that the method can effectively correct the Thevenin modeling error and improve SOC estimation accuracy. Furthermore, the proposed method is computationally simple and convenient for engineering applications without increasing model complexity.

Zooming-Free Hand-Eye Self-Calibration for Nanorobotic Manipulation Inside SEM

Nanorobotic manipulation inside Scanning Electron Microscope (SEM) is widely used in nanomaterial characterization and assembly tasks. However, tedious and time-consuming hardware regulations, like axis alignment, are still critical and mandatory to keep the functionality of nanorobot systems. This paper proposes a novel zooming-free hand-eye self-calibration method for nanorobotic manipulation inside SEM to circumvent the hardware regulation problem, which is applicable to a wide range of system configurations. The hand-eye relationship calibration method is based on the active incremental motion of the nanorobot without referring to expensive and complicated calibration patterns. Both perspective projection at low magnification and affine projection at high magnification are modelled, and unified by the image Jacobian matrix technique. With several times active calibration at different magnification factors in advance, the hand-eye relationship can be, thereafter, determined from the model at arbitrary status. The calibration model can be applied to both accurate SEM visual measurement and precision visual servo control. The corresponding methods regarding two-dimensional (2D) measurement, in-plane positioning, and constrained three-dimension (3D) positioning based on the proposed hand-eye relationship model are also presented in this paper. Experiments show that practical contact between robot end-effector and silicon nanowire under 1500× is well established with standard deviation less than one pixel.

Measurement and Application of VNA

The vector network analyzer is the most important measurement instrument in communication antennas. In order to characterize the microwave components and the front-end of wireless systems, the vector network analyzer is the most commonly used frequency domain measurement platform. The uncertainty analysis of its measurement results is one of the hot topics in the field of instrumentation and measurement. An in-depth analysis of measurement uncertainty introduced by different calibration methods is the quantitative basis for reasonable selections of self-calibration methods. In frequency domain channel measurement, multiple frequency points need to be measured, and there is a strong correlation between the measurement results of each frequency point, which effectively gives a reasonable choice of the measurement method. The impact of the effective assessment is combined with various experiments, tests and practical comparative analysis of the obtained data.

Self-Calibration for Sparse Uniform Linear Arrays with Unknown Direction-Dependent Sensor Phase by Deploying an Individual Standard Sensor

Calibration of the unknown direction-dependent (DD) sensor phase and aliasing-free directions of arrival (DOA) estimation for sparse linear arrays are difficult tasks. In this work, we deploy an individual standard sensor with a known sensor phase response along the axis of the uncalibrated sparse linear array, a self-calibration method is proposed, in which the unknown DD sensor phase and the aliasing-free DOAs are both estimated. The proposed method is realized with a two-step scheme. In the first step, the sensor phase is eliminated by the Kronecker product of the covariance matrices in two different frequency bins, and the frequency difference satisfies the spatial Nyquist sampling theorem. Then, the DOAs can be robustly estimated without the influences of grating lobes and unknown sensor phase parameters. In the second step, the steering matrix is estimated with the known phase parameters of the deployed standard sensor. Then, the DD sensor phase is extracted from the steering matrix using the DOAs obtained in the first step. Hence, the disadvantages of iteration-based strategies in conventional calibration algorithms (e.g., local minima convergence) can be avoided. The performance of the proposed method is evaluated using simulation data and compared with that of Cramer–Rao bounds.

High accurate self-calibration of photonic integrated circuit using lossless thermo-optic phase shifters.

The accurate calibration of large-scale switch networks is critical for integrated photonics, in which the integrated optical true time delay chip is typical. In this work, a novel self-calibration method without extra testing ports is proposed by introducing lossless thermo-optic phase shifters instead to calibrate the network. As a demonstration, a 5-bit delay line based on silicon nitride is fabricated and calibrated. The extinction ratio of all the switches is greater than 30.9 dB at the cross and bar states. Using this method, the 5-bit optical delay line which can be tuned in a range of 118.53 ps and reach a low delay time deviation less than ±0.4 ps.

Auto-calibration of robot workcells via remote laser scanning

• The remote robot calibration method that requires neither marker settings nor initial positioning of the measurement system was proposed. • The interplane relation feature extracted from point-cloud data was used to identify the relative differences between the coordinates of the 3D model and those of the real workcell. • We presented an iterative method to estimate world coordinates and increase the accuracy of the revised robot path. • The accuracy of adjusted robot program is measured to be 0.0817 mm based on a test of the pointing operation. For new robotic products, the rapid generation of robot programs is generally ensured by offline programming (OLP). Nevertheless, robotic calibration of workcells is essential because of the geometric differences between the three-dimensional (3D) model environment and the real world. In contrast with previous robot self-calibration methods that use specific sensors, the proposed method requires neither marker settings nor initial positioning of the measurement system. Instead, the geometric feature of interplane relations finds relative differences between the coordinates of the 3D model and those of the real workcell. In addition, an iterative method of estimating world coordinates is introduced to increase the accuracy of the revised robot path. A portable laser scanner acquires 3D point-cloud data, so it can be located near the workcell instead of being pre-fixed, thereby facilitating its use. Through experimentation, an average distance of 0.0817 mm is found between the OLP-teaching points and the calibrated points of the pointing operation.