Drift Compensation Research Articles

Improved calibration method of a four-quadrant detector based on Bayesian theory in a laser auto-collimation measurement system.

Reliable and accurate calibration for a four-quadrant detector (QD) is a prerequisite for high-accuracy laser auto-collimation measurements. However, the calibration accuracy is limited largely by the non-linearity of QD, especially for large-range detection. To address this issue, an improved calibration method of QD based on Bayesian theory in laser auto-collimation measurement is proposed in this paper. First, the non-linearity problem of QD is analyzed, and for accurate calibration of QD, a high-precision identification model based on Bayesian theory is presented. An analytical expression between the output signal of QD and the position of the laser spot is established, and then a calibration system with laser drift compensation to avoid influences from the laser source as a datum is constructed. A series of experiments is conducted to verify the performance of the improved calibration method. The results reveal that the improved method can effectively enhance the calibration accuracy of QD and reduce the residuals in root mean square error by 86% compared to the 15-order polynomial fitting over a detection range of ±1mm. The comparison experiments also demonstrate that the proposed calibration method has advantages over the conventional method in terms of accuracy and robustness.

Toward a Stochastic Drift Simulation Model for Graphene-Based Gas Sensors

Monitoring air quality in cities as well as indoors has become an important topic in recent years. Being easily and densely deployable and rather low-cost, chemiresistive gas sensors indicate a feasible technology for this problem, especially if they are assembled as e-noses tracking multiple gases in one device. Nonetheless, the long-term stability of such sensors poses a severe problem for their measurement accuracy, which substantiates the need for drift compensation procedures as well as robust algorithms in the gas prediction process. In order to test such drift compensation methods and to generate synthetic data for algorithm training, a simulation model would be highly useful. In this paper, we present our investigations towards a simulation framework aiming at generating typical sensor responses of a graphene-based gas sensor with an emphasis on its drift behavior. Different drift models are studied, implemented and compared to real measurement data from a lab setup. The evaluation shows promising results when compared to experiments from a real chemiresistive sensor to which the model was parametrized. By using a second setup with fundamentally different heating characteristics we observed that recalibration is a necessary step to generalize to different sensing modes and to ensure the overall quality of the simulation data. Overall, these investigations provide a proper basis towards tackling the challenges in drift simulation.

Compensation for wind drift during raptor migration improves with age through mortality selection.

Each year, billions of flying and swimming migrants negotiate the challenging displacement imposed by travelling through a flowing medium. However, little is known about how the ability to cope with drift improves through life and what mechanisms drive its development. We examined 3,140 days of migration by 90 GPS-tagged raptorial black kites (Milvus migrans) aged 1-27 years to show that the ability to compensate for lateral drift develops gradually through many more years than previously appreciated. Drift negotiation was under strong selective pressure, with inferior navigators subject to increased mortality. This progressively selected for adults able to compensate for current cross flows and for previously accumulated drift in a flexible, context-dependent and risk-dependent manner. Displacements accumulated en route carried over to shape the wintering distribution of the population. For many migrants, migratory journeys by younger individuals represent concentrated episodes of trait selection that shape adult populations and mediate their adaptation to climate change.

Neighborhood Preserving and Weighted Subspace Learning Method for Drift Compensation in Gas Sensor

This article presents a novel discriminative subspace-learning-based unsupervised domain adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning approaches assume that the gas sensor data follow a certain distribution such as Gaussian, which often does not exist in real-world applications. In this article, we address this issue by proposing a novel discriminative subspace learning method for DA with neighborhood preserving (DANP). We introduce two novel terms, including the intraclass graph term and the interclass graph term, to embed the graphs into DA. Besides, most existing methods ignore the influence of the subspace learning on the classifier design. To tackle this issue, we present a novel classifier design method (DANP+) that incorporates the DA ability of the subspace into the learning of the classifier. The weighting function is introduced to assign different weights to different dimensions of the subspace. We have verified the effectiveness of the proposed methods by conducting experiments on two public gas sensor datasets in comparison with the state-of-the-art DA methods.

A Method for Fiber Optic Gyroscope Temperature Drift Compensation Using Correlations between the Readings of the Gyroscope and Several Temperature Sensors

A Method for Fiber Optic Gyroscope Temperature Drift Compensation Using Correlations between the Readings of the Gyroscope and Several Temperature Sensors

Characterization and compensation of thermal bias drift of a stiffness-tunable MEMS accelerometer

This paper presents characterization and compensation of the thermal bias drift of a MEMS accelerometer whose effective stiffness is electrostatically tuned to a low value with the help of the single-sided parallel plates (SSPPs) capacitors. A temperature drift model for the proposed accelerometer is built and the temperature effect on the mechanical stiffness and the circuit gains is characterized. The characterization reveals that the variations of the feedforward coefficient including the mechanical stiffness and the readout gain due to the temperature change play a significant role in the bias drift of output of the proposed closed-loop accelerometer. On the other hand, the variations of both feedback gain and tuning gain as a function of temperature are one-order-of-magnitude less than that of the feedforward coefficient. To calibrate the feedforward coefficient, an excitation signal as a form of AC reference displacement is introduced to drive the proof mass and the readout response is demodulated to calibrate the variance of the feedforward coefficient correspondingly. An open-loop compensation scheme for the temperature-induced bias drift using the variance of the calibrated response is proposed accordingly. Alternatively, the calibrated response can be controlled to a preset value by a new proportion integral derivative (PID) controller by the adjustment of the SSPP tuning voltage. The output of the PID controller is used for the calibration of the introduced bias from the electrostatic tuning force. Therefore, a closed-loop compensation of the thermal bias drift is achieved by remaining the feedforward coefficient while eliminating the calibrated bias. Experiments under a temperature cooling process and different tuning voltages are carried out to verify two proposed compensation schemes. The temperature drift coefficients (TDCs) of both compensated outputs exhibit at least one-order-of-magnitude reduction in response to the temperature change. When the effective stiffness of the accelerometer is tuned to about 4.19 N m−1, the TDC of the compensated output using the proposed open-loop or closed-loop compensation schemes is reduced to about 0.1136 or 0.0391 mg °C−1, respectively and meanwhile, the Allan bias instability is slightly increased to 48.63 or 56.14 μg, respectively.

A Smartphone-Based Sensor With an Uncooled Infrared Thermal Camera for Accurate Temperature Measurement of Pig Groups

Low-cost uncooled infrared thermal cameras show a large application potential for the rapid diagnosis of pig diseases. However, with the increase in ambient temperature and absorbing infrared radiation, almost all of them produce severe thermal drift and provide poor accuracy of temperature measurement. In addition, the unknown surface emissivity on a pig’s body can also bring measuring errors. In this article, an uncooled infrared thermal camera with an accuracy of 3°C was used to develop a smartphone-based sensor for measuring the pig’s temperature. Based on this sensor, we proposed a system combined with internal calibration for real-time compensation of the thermal drift and altering spectrum for eliminating the influence of unknown surface emissivity to improve the accuracy of temperature measurement. After calibration, the accuracy of this sensor was improved from 3 to 0.3°C without knowing the emissivity. We used this sensor to successfully identify pigs with abnormal temperatures on a livestock farm, which indicated that our proposed methods may be widely employed for rapid temperature screening and diagnosis of pig diseases.

On-line drift compensation for continuous monitoring with arrays of cross-sensitive chemical sensors

Long-term application of chemical sensor arrays for continuous monitoring is challenging as a result of sensor drift. Drift correction often requires periodic recalibration, which may not be feasible for sensors deeply embedded and deployed for uninterrupted continuous monitoring. In this paper, we propose a multi-calibration ensemble approach to compensate for sensor drift in such applications. Our method uses past sensor measurements for which ground-truth is available, and treats them as “pseudo-calibration” samples. With these, it builds a regression model to predict the concentration of target analytes by combining (1) the current sensor measurements and (2) the history of prior pseudo-calibration samples. We evaluate the efficacy of the proposed model using three different regression techniques, partial least squares, extreme gradient boosting, and neural networks, and compare it against two baselines: regression models that do not use the pseudo-calibration samples, and a state-of-the-art drift-correction technique. We evaluated these models on an experimental dataset from a bioprocess control application, and characterize them as a function of cross-sensitivity in the sensor array and amount of drift through computer simulations. The proposed approach outperforms both baselines on the experimental dataset, and under all simulation conditions, achieving significantly lower normalized root mean square errors in the prediction of target variables. These results hold for the three regression models used, which indicates that the proposed approach is agnostic to the underlying regression model.

Laser source frequency drift compensation in Φ-OTDR systems using multiple probe frequencies.

Fully distributed fiber sensors, such as phase sensitive optical time domain reflectometry (Φ-OTDR) systems, have drawn significant attention from researchers, especially for use in geophysical applications. Distributed sensing, cost efficiency, wide dynamic range, good spatial resolution, and high accuracy make these sensors ideal for industrial use and for replacing traditional geophones. However, inevitable drifts in the central frequency of laser sources always cause low frequency noise in the output, which could easily be mistaken with real sub-Hertz environmental vibrations. This deteriorates the data accuracy, especially when dealing with low frequency seismic waves. In this study, we propose a method in which adding an extra probe frequency to a Φ-OTDR setup provides a reference frequency. This reference frequency provides information regarding changes in the laser source and other environmental noises, such as humidity and temperature, helping to refine extracted results from low frequency noise. This feature is also very useful for frequency domain analysis, where we may lose the near DC band information during mathematical measurements. Regarding the adjustable properties of this reference frequency, it can be implemented in various Φ-OTDR applications and commercial devices.

Research on constant force grinding control of aero-engine blades based on extended state observer

PurposeConsidering the influence of environmental noise and modeling error during the process of the robotic automatic grinding aero-engine blade, this study aims to propose a method based on the extended state observer (ESO) to reduce the fluctuation of normal grinding force.Design/methodology/approachFirst, the measurement range of the six-dimensional force sensor is calibrated according to the maximum acceleration of end-effector and grinding force. Second, the gravity and zero drift compensation model is built to compensate for measurement error. Finally, the switching function is designed based on the difference between the expected grinding force and the actual feedback value. When the value of function stays within the switching band, a nonlinear active disturbance rejection control (ADRC) loop is applied. When the function value reaches outside the switching band, an ESO-based sliding mode control (SMC) loop is applied.FindingsThe simulated and experimental results show that the proposed control method has higher robustness compared with proportion-integral-derivative (PID), Fuzzy PID and ADRC.Research limitations/implicationsThe processing parameters of this paper are obtained based on the single-factor experiment without considering the correlation between these variables. A new control strategy is proposed, which is not only used to control the grinding force of blades but also promotes the development of industrial control.Originality/valueESO is used to observe environmental interference and modeling errors of the system for real-time compensation. The segment control method consisting of ESO-based SMC and ESO-based ADRC is designed to improve the robustness. The common application of the two parts realizes suppression of fluctuation of grinding force.

Augmented reality surgical navigation system based on the spatial drift compensation method for glioma resection surgery.

The number of patients who suffer from glioma has been increasing, and this malignancy is a serious threat to human health. The mainstream treatment for glioma is surgical resection; therefore, accurate resection can improve postoperative patient recovery. Many studies have investigated surgical navigation guided by mixed reality, with good outcomes. However, the limitations of mixed reality, such as spatial drift caused by environmental changes, limit its clinical application. Therefore, we present a mixed reality surgical navigation system for glioma resection. Preoperative information can be fused precisely with the real patient with the spatial compensation method to achieve clinically suitable accuracy. A head-mounted device was used to display virtual information, and a markerless spatial registration method was applied to precisely align the virtual anatomy with the real patient preoperatively. High-accuracy preoperative and intraoperative movement and spatial drift compensation methods were used to increase the positional accuracy of the mixed reality-guided glioma resection system when the patient's head is fixed to the bed frame. Several experiments were designed to validate the accuracy and efficacy of this system. Phantom experiments were performed to test the efficacy and accuracy of this system under ideal conditions, and clinical tests were conducted to assess the performance of this system in clinical application. The accuracy of spatial registration was 1.18mm in the phantom experiments and 1.86mm in the clinical application. Herein, we present a mixed reality-based multimodality-fused surgical navigation system for assisting surgeons in intuitively identifying the glioma boundary intraoperatively. The experimental results indicate that this system has suitable accuracy and efficacy for clinical usage.

Polarization based discrete variables quantum key distribution via conjugated homodyne detection

Optical homodyne detection is widely adopted in continuous-variable quantum key distribution for high-rate field measurement quadratures. Besides that, those detection schemes have been being implemented for single-photon statistics characterization in the field of quantum tomography. In this work, we propose a discrete-variable quantum key distribution (DV-QKD) implementation that combines the use of phase modulators for high-speed state of polarization (SOP) generation, with a conjugate homodyne detection scheme which enables the deployment of high speed QKD systems. The channel discretization relies on the application of a detection threshold that allows to map the measured voltages as a click or no-click. Our scheme relies also on the use of a time-multiplexed pilot tone—quantum signal architecture which enables the use of a Bob locally generated local oscillator and opens the door to an effective polarization drift compensation scheme. Besides that, our results shows that for higher detection threshold values we obtain a very low quantum bit error rate (QBER) on the sifted key. Nevertheless, due to huge number of discarded qubits the obtained secure key length abruptly decreases. From our results, we observe that optimizing the detection threshold and considering a system operating at 500 MHz symbol generation clock, a secure key rate of approximately 46.9 Mbps, with a sifted QBER of 1.5% over 40 km of optical fiber. This considering the error correction and privacy amplification steps necessary to obtain a final secure key.

Self-Calibration Technique with Lightweight Algorithm for Thermal Drift Compensation in MEMS Accelerometers.

Capacitive MEMS accelerometers have a high thermal sensitivity that drifts the output when subjected to changes in temperature. To improve their performance in applications with thermal variations, it is necessary to compensate for these effects. These drifts can be compensated using a lightweight algorithm by knowing the characteristic thermal parameters of the accelerometer (Temperature Drift of Bias and Temperature Drift of Scale Factor). These parameters vary in each accelerometer and axis, making an individual calibration necessary. In this work, a simple and fast calibration method that allows the characteristic parameters of the three axes to be obtained simultaneously through a single test is proposed. This method is based on the study of two specific orientations, each at two temperatures. By means of the suitable selection of the orientations and the temperature points, the data obtained can be extrapolated to the entire working range of the accelerometer. Only a mechanical anchor and a heat source are required to perform the calibration. This technique can be scaled to calibrate multiple accelerometers simultaneously. A lightweight algorithm is used to analyze the test data and obtain the compensation parameters. This algorithm stores only the most relevant data, reducing memory and computing power requirements. This allows it to be run in real time on a low-cost microcontroller during testing to obtain compensation parameters immediately. This method is aimed at mass factory calibration, where individual calibration with traditional methods may not be an adequate option. The proposed method has been compared with a traditional calibration using a six tests in orthogonal directions and a thermal chamber with a relative error difference of 0.3%.

A calibrant-free drift compensation method for gas sensor arrays

Gas sensors lack repeatability over time. They are affected by drift, the result of changes at the sensor level and in the environment. A solution is to design software methods that compensate for the drift. Existing methods are often based on calibration samples acquired at the start of each new measurement session. However, finding a good reference compound is a difficult task and generating calibration samples is time-consuming. We propose a model-based correction method which does not require any calibration sample over time, operating ‘blindly’. In this study, we focus on the drift affecting electronic noses. To this end, we built a real data set acquired over 9 months in real-life conditions. By using the proposed method, we show that the drift is partly compensated, thus increasing the reliability of the electronic nose. Besides, we also show that the algorithm can easily adapt if the target compounds are not all sampled during every session.

Multidimensional Multiconvolution-Based Feature Extraction Approach for Drift Tolerant Robust Classifier for Gases/Odors

Recently, convolutional neural networks (CNNs) have been used for the classification of gases/odors. These methods involve statistical algorithms for drift compensation of sensor characteristics inhibiting its application in real time. In this letter, we have proposed a hybrid CNN model called “drift tolerant robust classifier (DTRC),” which extracts multidimensional features from the raw sensor array responses and automatically compensates for any drift in the sensor response characteristics. The proposed DTRC comprises of 1-D, 2-D, and 3-D convolutional layers in a hybrid manner to compensate for the referred drift without any statistical algorithm. The efficacy of DTRC has been evaluated on a popular dataset and its published results, which comprise of ten batches of sensor characteristics exhibiting drift over a period of three years. Our proposed DTRC outperformed the referred results. In another experiment, DTRC outperformed other state-of-the-art methods. The proposed CNN architecture (DTRC) is a simpler, lightweight CNN with multidimensional multiconvolution end-to-end architecture, suitable for real-time applications.

Temperature drift compensation of fiber strapdown inertial navigation system based on GSA-SVR

The fiber strapdown inertial navigation system (INS) has been widely utilized in aircraft, autonomous driving, robotics, and other fields due to the rapid growth of fiber technology. The core components of INS, the fiber optic gyroscope (FOG) and quartz flexible accelerometer (QFA), are prone to affected by the environmental temperature variation. The traditional temperature drift compensation method requires a considerable time to calibrate each inertial sensor individually. But after the six inertial sensors are integrated, the temperature environment inside the INS will become more complex. As a result, this device-level method has an unsatisfactory effect in the practical application of INS. Furthermore, the traditional method ignores the fact that the temperature drift characteristics of FOG are not the same at different working states. To solve the above problems, we propose a novel temperature drift compensation method of INS based on gravitational search algorithm (GSA) tuning support vector regression (SVR). The startup state and stable working state are modeled independently based on the FOG's features. Without adding additional thermometers to the inertial sensors, the temperature field is constructed by making full use of the information provided by the built-in thermometer of six inertial sensors. The experimental results verify the effectiveness of our method during different working states. And compared with the traditional polynomial fitting method, our method has better performance in the navigation experiment, the navigation accuracy increased by more than 50%.

Visual-Inertial Cross Fusion: A Fast and Accurate State Estimation Framework for Micro Flapping Wing Rotors

Real-time and drift-free state estimation is essential for the flight control of Micro Aerial Vehicles (MAVs). Due to the vibration caused by the particular flapping motion and the stringent constraints of scale, weight, and power, state estimation divergence actually becomes an open challenge for flapping wing platforms’ longterm stable flight. Unlike conventional MAVs, the direct adoption of mature state estimation strategies, such as inertial or vision-based methods, has difficulty obtaining satisfactory sensing performance on flapping wing platforms. Inertial sensors offer high sampling frequency but suffer from flapping-introduced oscillation and drift. External visual sensors, such as motion capture systems, can provide accurate feedback but come with a relatively low sampling rate and severe delay. This work proposes a novel state estimation framework to combine the merits from both to address such key sensing challenges of a special flapping wing platform—micro flapping wing rotors (FWRs). In particular, a cross-fusion scheme, which integrates two alternately updated Extended Kalman Filters based on a convex combination, is proposed to tightly fuse both onboard inertial and external visual information. Such a design leverages both the high sampling rate of the inertial feedback and the accuracy of the external vision-based feedback. To address the sensing delay of the visual feedback, a ring buffer is designed to cache historical states for online drift compensation. Experimental validations have been conducted on two sophisticated microFWRs with different actuation and control principles. Both of them show realtime and drift-free state estimation.

TDACNN: Target-domain-free domain adaptation convolutional neural network for drift compensation in gas sensors

Sensor drift is a long-existing unpredictable problem that deteriorates the performance of gaseous substance recognition, calling for an antidrift domain adaptation algorithm. However, the prerequisite for traditional methods to achieve fine results is to have data from both nondrift distributions (source domain) and drift distributions (target domain) for domain alignment, which is usually unrealistic and unachievable in real-life scenarios. To compensate for this, in this paper, deep learning based on a target-domain-free domain adaptation convolutional neural network (TDACNN) is proposed. The main concept is that CNNs extract not only the domain-specific features of samples but also the domain-invariant features underlying both the source and target domains. Making full use of these various levels of embedding features can lead to comprehensive utilization of different levels of characteristics, thus achieving drift compensation by the extracted intermediate features between two domains. In the TDACNN, a flexible multibranch backbone with a multiclassifier structure is proposed under the guidance of bionics, which utilizes multiple embedding features comprehensively without involving target domain data during training. A classifier ensemble method based on maximum mean discrepancy (MMD) is proposed to evaluate all the classifiers jointly based on the credibility of the pseudolabel. To optimize network training, an additive angular margin softmax loss with parameter dynamic adjustment is utilized. Experiments on two drift datasets under different settings demonstrate the superiority of TDACNN compared with several state-of-the-art methods.

Modelling and fabrication of wide temperature range Al0.24Ga0.76As/GaAs Hall magnetic sensors

Silicon Hall-effect sensors have been widely used in industry and research fields due to their straightforward fabrication process and CMOS compatibility. However, as their material property limitations, technicians usually implement complex CMOS circuits to improve the sensors’ performance including temperature drift and offset compensation for fitting tough situation, but it is no doubt that it increases the design complexity and the sensor area. Gallium arsenide (GaAs) is a superior material of Hall-effect device because of its large mobility and stable temperature characteristics. Concerning there is no specified modelling of GaAs Hall-effect device, this paper investigated its modelling by using finite element method (FEM) software Silvaco TCAD® to help and guide GaAs Hall-effect device fabrication. The modeled sensor has been fabricated and its experimental results are in agreement with the simulation results. Comparing to our previous silicon Hall-effect sensor, the GaAs Hall-effect sensor demonstrates potential and reliable benchmark for the future Hall magnetic sensor developments.

Full polarization random drift compensation method for quantum communication

Long-term quantum key distribution (QKD) using polarization encoding requires a random drift compensation method. We propose a method to compensate any state of polarization based on the quantum bit error rate (QBER) of two states from two non-orthogonal mutually unbiased bases. The proposed method does not require dedicated equipment, and through a simple but highly efficient feedback loop it compensates the polarization random drift suffered by photons while transmitted over the optical fiber quantum channel. A QBER lower than 2% was observed even considering imperfect single photon detectors. Besides, we verify a 82% secret key rate generation improvement in a finite-key size BB84 implementation for a 40 km fiber-optics quantum channel.