Sensor Errors Research Articles

Measurement Error Model and Compensation Algorithm for Bionic Polarization Orientation

Division of focal plane (DoFP) polarization image sensors can obtain a complete set of polarization information at every image. Because of the high acquisition efficiency, these sensors are increasingly used in bionic polarization orientation. However, the resolution loss of image and instantaneous field-of-view (IFOV) errors of DoFP sensors will lead to measurement errors in polarization information, which will reduce the orientation accuracy of DoFP sensors. In this paper, we reveal how these errors will affect the measurement accuracy of polarization information and orientation accuracy. The measurement error model of polarization information is then established, simulated, and analyzed. Finally, a compensation algorithm is proposed for the polarization orientation. The outdoor experimental results illustrate that the proposed method can effectively reduce the measurement errors of polarization information and improve orientation accuracy.

Errors of sensors for conversion of multi-phase currents into voltage in power supply systems

The paper presents the structure of the sensor for converting multi-phase primary currents into secondary voltages of reactive power of power supply networks, the algorithm for modeling processes in the sensor that sends a signal to the primary current monitoring and control devices provides a highly formalized graph model based on an analytical expression, static characteristics and sensor errors studied on the basis of its analytic expression.

Underground Sedimentary Rock Moisture Permeation Damage Assessment Based on AE Mutual Information

Moisture permeation would lead to damage accidents. It is of great significance to measure and monitor the moisture states of underground sedimentary rock engineering in real time. In this research, a reliability assessment method for underground rock based on acoustic emission (AE) mutual information was proposed. First, the mutual relationship between multiple AE parameters and moisture permeation damage was analyzed. Then the mechanical parameters were fit by the probabilistic method, so that moisture permeation damage could be assessed with a data-driven approach. The experiment showed that this method could identify samples with different moisture permeation damage levels. The moisture permeation damage could be predicted in the early stage. Moreover, this method could reduce sensor error through probabilistic analysis.

Research on a High-Precision Measurement Algorithm Using FOG-Based Single-Axis RINS for Core Drilling

It is much important for core drilling to measure the orientation and trajectory of the drill bit precisely. Presently, with the higher and higher accuracy requirements of orientation and positioning, the utilization of inertial navigation system, especially the use of fiber optic gyroscope (FOG), has become a general trend. However, the realization of high precision and small size simultaneously is still a difficult challenge. In this paper, a FOG-based single-axis rotational inertial navigation system (RINS) instrument was used to suppress the inertial sensor errors automatically through rotation modulation technology. Based on the limited movement of the drill bit in lateral and vertical directions, this paper proposed a new dead reckoning (DR) algorithm which performed much better than normal inertial navigation algorithm due to the reduction of error sources. But the actual accuracy of DR algorithm was affected by the installation errors between the axes of the instrument and the pipeline. Therefore, this paper further proposed a new and practical calibration method of the installation errors to improve the accuracy of DR algorithm. To verify the rationality and the validity of the algorithm, the simulations and semi-physics simulation experiments were conducted under laboratory conditions. The results showed that the proposed DR algorithm with installation errors corrected could reach a high positioning accuracy of 0.5% utilizing FOGs with a drift of 0.015°/h. This study provided an innovative solution for high positioning precision based on a small-sized and low-cost RINS instrument in core drilling.

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. Assuming the model structure has been identified, in this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.

Remote Microphone Technique for Active Noise Control Over Distributed Networks

Multichannel Active Noise Control (ANC) headrest systems have usually been designed with the objective of creating quiet areas at the passenger positions within the cabin of a public transport. Due to the high computational demands of dealing with multiple loudspeakers and multiple microphones by using a single centralized processor, and the convenience of a scalable system, a distributed implementation may be advisable. On the other hand, the addition of the remote microphone (RM) technique to the ANC headrest systems has recently allowed the creation of local zones of quiet at unachievable placements for the error sensors. This technique allows the quiet zone to be shifted to a desired location away from the physical sensor. However, to our knowledge, the implementation of an ANC headrest system with virtual microphones over a distributed acoustic sensor network has not been addressed. In this work, a distributed version of the multiple-error FxLMS (MEFxLMS) algorithm considering the RM method (RM-DMEFxLMS) is proposed by using an alternative formulation of the RM method. Through a set of simulations, it is found that on a non-constrained communication network, the proposed algorithm presents the same performance of its centralized version and provides a scalable and more versatile ANC system.

Heart Attack Notification and Monitoring System Using Internet of Things

People are frequently shocked when someone passes away suddenly without any prior symptoms. One of the contributing factors is a heart attack. This condition might occur anywhere and at any time. A sudden heart attack can be highly perilous for a person who is alone, without family members or friends because the family cannot be informed of the victim's condition or their location. Therefore, it is vital to raise awareness of heart attacks. With the support of the Internet of Things, this study aims to develop a wearable device that people may use to monitor their heart health and connect with hospitals to get alerts in case of a heart attack. This system also provides family members with access to a web-based patient monitoring tool. The heart beat is considered as the parameter in developing this system. There are three types of evaluation which are conducted in this study, namely: 1) Sub-system evaluation; 2) Black-box testing; and 3) Integrating system testing. The three evaluation results show that all assembled hardware components are work properly and the system effectively satisfies the objectives of monitoring, buzzer activation, hospital and patient family notification, and so forth, with 1.96% average sensor error, which is still considerably acceptable.

Mathematical model of instrumental vibration error of angular velocity sensor

The problem of mathematical description of the influence of harmful deterministic and broadband random vibrations of a moving object on the measurement result of the sensor of the angular velocity of rotation of this object installed on it is considered. It is shown that under such conditions a systematic instrumental vibration error arises in the angular velocity sensor. The source of this error is the nonlinearity of the static conversion function of the angular velocity sensor and the asymmetry of its conversion coefficient.
 A mathematical model of this error has been obtained. Formulas for calculating the vibrational error of the angular velocity sensor are obtained depending on the apparent angular velocity of the flight of the aircraft, the vibration parameters of the base on which the sensor is installed and the parameters of the nonlinear sensor conversion function.
 This model makes it possible to calculate the value of the vibration error under specified vibration conditions for a specific angular velocity sensor (direct analysis problem), as well as to select an angular velocity sensor based on the values ​​of its conversion function coefficients, based on ensuring the required accuracy of measuring the angular velocity of a moving object using this angular velocity sensor (inverse synthesis problem).

Accuracy estimation of the calculating algorithm the satellite’s orbit of earth remote sensing

Transformations of the coordinate systems in which the reference vectors of the initial navigation information - the flow of sunlight and the Earth's magnetic field are set - into the coordinate system of the sun-synchronous orbit of the Earth's remote sensing satellite by matrix and analytical methods are considered. The influence of the accuracy of calculating the direction cosines of the satellite orientation matrices on the accuracy of determining its orientation angles and the scanning accuracy is estimated. The estimation of the influence on the accuracy of determining the satellite orientation of the sensor errors of the primary navigation information and the features of the matrix orientation algorithm is carried out. Analytical dependencies are obtained that allow one to evaluate the influence of the noted factors depending on the position of the satellite in its orbit and the motion of the Earth along the ecliptic.

Graph convolutional networks for traffic forecasting with missing values

Traffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors. The Spatio-temporal feature in traffic data brings more challenges for processing such missing values, for which the classic techniques (e.g., data imputations) are limited: (1) in temporal axis, the values can be randomly or consecutively missing; (2) in spatial axis, the missing values can happen on one single sensor or on multiple sensors simultaneously. Recent models powered by Graph Neural Networks achieved satisfying performance on traffic forecasting tasks. However, few of them are applicable to such a complex missing-value context. To this end, we propose GCN-M, a Graph Convolutional Network model with the ability to handle the complex missing values in the Spatio-temporal context. Particularly, we jointly model the missing value processing and traffic forecasting tasks, considering both local Spatio-temporal features and global historical patterns in an attention-based memory network. We propose as well a dynamic graph learning module based on the learned local-global features. The experimental results on real-life datasets show the reliability of our proposed method.

AN ADAPTIVE SOFT SENSOR DESIGN BASED ON A NEURAL NETWORK WITH A PREDICTIVE FILTER IN THE FEEDBACK LOOP FOR A REACTION DISTILATION TECHNOLOGICAL PROCESS

Rectification and reactive distillation columns are the main of all units in the petrochemical and refining industry. Soft sensors consist of mathematical models that estimate of the quality of an output product in real time are used for technological processes control. In general, changes in the composition of raw materials, catalyst deactivation, etc. result in inconsistency between obtained data and the current state of the technological process. Soft sensor design obtained on such data will loss of accuracy in estimating the necessary parameters of the output product. An adaptive soft sensor design based on a neural network with a predictive filter in the feedback loop for solve the mismatching obtained data and the current state of the technological process problem is proposed. “Moving window” conception is used for size window adapting to the actual state of the technological process. Parameter estimation based on a neural network using data matching to the technological process. A predictive filter in the feedback loop for improve the estimation accuracy of the quality parameter at the cost of predicting the error of the soft sensor designed is proposed. A comparative analysis of several adaptive soft sensors based on neural networks using the "moving window" conception for estimation a by-product concentration of in the output product of the reaction-distillation column and the effectiveness of the proposed approach are shown. Application of the predictive filter in the feedback loop allows to improve the accuracy of the soft sensor based on a neural network by 12.94 % (coefficient of determination) and by 39.81 % (mean absolute error) in comparison with that of without predictive filter.

Average torque distribution strategy for low speed operation of three-phase SRM with low-resolution rotor position sensor

An average torque distribution (ATD) strategy for low-speed operation of three-phase switched reluctance motor (SRM) with hall-effect rotor position sensor, as well as a calibration method for the sensor, are proposed. The sensor can only detect unaligned and aligned positions of all phases, and these positions evenly divides an electrical cycle into six position sectors. These positions are fully utilized by shaping a phase current as a four-step waveform and controlling motor average torque in each sector. In commutation sectors, a motor average torque reference is distributed into two phase average torque references. The distribution is optimized offline to minimize torque ripple and copper losses based on the phase torque characteristics. Compared with the torque control schemes using square-wave current references, the proposal can greatly reduce torque ripple without or with only a small penalty on copper losses. Based on difference among the aligned position detection errors of the hall-effect sensor, a calibration procedure for the sensor is developed. This procedure can ensure the absolute value of the average of these errors less than the maximum difference among these errors without high-resolution position signal as reference. Experimental results demonstrate effectiveness of the proposed ATD strategy and the calibration.

System noise variance matrix adaptive Kalman filter method for AUV INS/DVL navigation system

The Inertial Navigation System (INS)/Doppler Velocity Log (DVL) navigation system is capable of locating the Autonomous Underwater Vehicle (AUV) in real-time. However, inherent errors of sensors, especially the inertial measurement unit (IMU) measurement noise changes during navigation, make the system noise variance matrix unknown and time-dependent, resulting in inaccurate Kalman filter estimates of velocity and reducing the accuracy of the navigation system. To solve this problem, this paper proposes an adaptive system noise variance matrix Kalman Filter method for AUV INS/DVL navigation system. In this method, the statistical characteristics of the IMU measurement noise are estimated by distinguishing the AUV motion information obtained by the IMU measurement and the IMU measurement noise by the frequency domain analysis method. The Kalman filter velocity estimation accuracy is improved by adaptively adjusting the system noise variance matrix based on the IMU measurement noise. Six different sets of trajectory experiments were used to validate the effectiveness and applicability of the algorithm proposed in this paper. The experimental results show that the improved KF algorithm improves the positioning accuracy by an average of 2.29‰ navigation distance compared with the traditional KF algorithm, which proves that the proposed method can improve navigation performance.

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

AbstractDead reckoning accuracy determines the operation quality of pipeline mobile robot to a certain extent. With the progress of the industry, the research on the dead reckoning system of pipeline robot is more and more in‐depth, but how to improve dead reckoning accuracy is still a difficult problem for pipeline mobile robot. In this paper, to overcome the sensor defects of the traditional dead reckoning system, an error backpropagation neural network (BPNN) is introduced to compensate the sensor error caused by static drift and poor dynamic response. To reduce the interference of noise information, the covariance matching technique based on the Adaptive Neuro‐Fuzzy Inference System is used to estimate the measurement noise information. And then the two are combined in the algorithm based on the extended Kalman Filter (EKF) framework for data fusion. An EKF algorithm based on adaptive neural fuzzy combined with BPNN is proposed. Finally, a complete three‐dimensional dead reckoning system is established by coordinate transformation. The motion experiment of the quadruped wall‐climbing robot in the pipeline proves that the proximity of the calculated trajectory is not more than 6.00%, and the lowest is 1.07%, which effectively improves the reliability, robustness, and positioning accuracy of the dead reckoning.

Analysis and Correction of Measurement Error of Spherical Capacitive Sensor Caused by Assembly Error of the Inner Frame in the Aeronautical Optoelectronic Pod.

The ball joint is a multi-degree-of-freedom transmission pair, if it can replace the inner frame in the aviation photoelectric pod to carry the optical load, which will greatly simplify the system structure of the photoelectric pod and reduce the space occupied by the inner frame. However, installation errors in ball joint siting introduce nonlinear errors that are difficult to correct and two degree of freedom angular displacement of the ball joint is difficult to detect, which limits application in the precision control of two degrees of freedom systems. Studies of spherical capacitive sensors to date have not tested sensors for use in an inner frame stabilisation mechanism nor have they analysed the influence of installation error on sensor output. A two-axis angular experimental device was designed to measure the performance of a ball joint capacitive sensor in a frame stabilisation mechanism in an aeronautical optoelectronic pod, and a mathematical model to compensate for ball joint capacitive sensor installation error was created and tested. The experimental results show that the resolution of the capacitive sensor was 0.02° in the operating range ±4°, the repeatability factor was 0.86%, and the pulse response time was 39 μs. The designed capacitive sensor has a simple structure, high measurement accuracy, and strong robustness, and it can be integrated into ball joint applications in the frames of aeronautical photoelectric pods.

An enhanced positioning technique for underground pipeline robot based on inertial Sensor/Wheel odometer

For traditional underground pipeline robot based on the inertial sensor/wheel odometer, the outliers of the data collected by the positioning system, the attitude error of the inertial sensor and the velocity measurement error of the wheel odometer are crucial factors affecting positioning accuracy of the pipeline robot, and have a significant influence on underground pipeline maintenance and pipeline mapping. To solve the above three error factors, an enhanced positioning method for the underground pipeline robot based on the inertial sensor / wheel odometer is proposed in this paper. Firstly, basic principles including robot attitude calculation based on inertial sensors, robot velocity calculation based on wheel odometer, position calculation of pipeline robot and outliers elimination based on extrapolation fitting method are introduced; secondly, the velocity information and attitude angle of the pipeline robot is corrected by prior-backtracking method; thirdly, the round-trip positioning data optimization method based on adaptive weight is designed to improve the accuracy of the whole pipeline robot positioning system; finally, three pipelines with different lengths are introduced to verify the effectiveness and reliability of the proposed method. The experimental results show that the error of position information is no more than 0.18%, 0.07% and 0.11% of survey length, respectively.

The development and evaluation of a combined initial alignment algorithm for strapdown inertial navigation system

This paper proposes a combined initial alignment algorithm for strapdown inertial navigation system, in which the initial alignment is carried out in two stages during the process of preflight preparations while the aircraft is not moving. This method of initial alignment has the advantages of being conducted based on the Earth’s equatorial (Greenwich) coordinate system, which is also used by the aircraft onboard navigation system. In the first stage, the coarse initial alignment is carried out, and this is done according to the orientation parameters using the vector matching method. Then, in the second stage, a fine procedure is carried out, which may be looked at as an integrated system implemented using optimal Kalman filter. In this case, the initial parameters of orientation and navigation are refined, as well as the systematic errors of the inertial sensors are estimated and corrected. The simulation results show that the required level of initial alignment accuracy could be achieved by applying coarse and fine initial alignment procedures not sequentially but in a combination.

Radar-Based Multisensor Fusion for Uninterrupted Reliable Positioning in GNSS-Denied Environments

Multi-sensor integration is necessary to provide high-precision navigation solutions for autonomous vehicles. Land-vehicles often rely on global navigation satellite systems (GNSS) to acquire its position. However, there are some environments where GNSS signals are unavailable, such as indoor-parking garages, tunnels, and under bridges. Additional sensors are required to allow reliable positioning regardless of location. The vehicle’s on-board low cost inertial sensors (accelerometers and gyroscopes) are used for positioning in GNSS-denied environments. Despite their fidelity in short-term usage, inertial navigation systems (INS) are susceptible to drifts in their positioning solution due to the inherent inertial sensor errors, causing positioning errors over time in prolonged scenarios, such as indoor-parking garages. Modern land-vehicles can be equipped with a diversified set of perception systems (e.g. LiDAR, cameras) to provide information about the surrounding environment. These systems are well-studied in literature to provide accurate positioning. Yet, the performance of these systems may degrade in weather conditions such as heavy snow or rain, and may fail in degraded vision environments. The proposed research addresses some of the limitations of current positioning technologies for land vehicles by integrating low-cost on-board motion sensors with the all-weather electronic scanning radar (ESR) systems presently used in adaptive cruise control. This research employs a method of estimating the vehicle position based on multiple ESR systems. Integration with the on-board motion sensors guarantees continuous positioning estimation for uninterrupted navigation. The multi-sensor system integration utilizes extended Kalman filter (EKF) with a unique dynamic tuning approach for reliable and uninterrupted positioning. The proposed solution was examined in real GNSS-denied scenario of an indoor parking. This research has reached an uninterrupted self-contained EKF-based multi-sensor positioning system providing less than 2m error 90% of the time in a GNSS-denied environment for up to four minutes.

Error analysis and compensation method of underground turbine motor voltage sensor

the control system of petroleum drilling engineering has been in a bad and high temperature environment for a long time. In the unknown downhole working environment, the signal detection accuracy of turbine motor determines the drilling quality and cost of rotary steering drilling engineering. In order to design a high-temperature voltage sensor that meets the needs of oil drilling engineering, this paper takes 206B voltage sensor as the experimental object, simulates the downhole environment, carries out high-temperature experiments on this type of sensor, and analyzes the influencing factors of its error. The experimental data show that the downhole turbine motor voltage sensor is most affected by temperature, and the 206B sensor can withstand a maximum temperature of 125 °C. In view of its temperature influence, the least square method is used to compensate the error. The compensated voltage sensor error is reduced to 1~3.048%, which effectively improves the accuracy of the turbine motor voltage detection device and can accurately receive the downward signal of rotary steering drilling, so as to greatly reduce the drilling cost.