Sensor Uncertainty Research Articles

Influence of Geometric Properties of Capacitive Sensors on Slope Error and Nonlinearity of Displacement Measurements.

Capacitive sensors are widely used in industrial applications, such as CNC machine tools, where reliable positioning in the micrometer range with nanometer accuracy is required. Hence, these sensors are operated in harsh industrial environments. The accuracy of these sensors is mainly limited by slope errors and nonlinearities. In practice, the required accuracy of these sensors is achieved by a calibration against a metrological high-quality reference such as interferometric displacement measurement systems. This usually involves the use of high-order polynomials as calibration functions based on empirical data. In metrology, this is only the second-best approach and has disadvantages in terms of stability over the measurement range of the instrument. In addition, the validity of these empirical calibrations over time is questionable, and the associated uncertainty can only be roughly estimated. This makes regular recalibration of such sensors at short intervals mandatory to ensure the reliability of the displacement measurement. In this paper, we report on our investigations of the different parameters that affect the accuracy of capacitive sensors. Since the capacitance of these sensors results from the electric fields that build up between the electrodes, these field lines are calculated using FEM simulation models for typical commercial sensors. In the following the influence of various geometric parameters such as edge radius, guard ring size and shape, or thickness of the electrodes are individually analyzed according to their impact on the accuracy of these sensors. Based on these simulations, the deviations of the capacitance as they arise for real detector geometries can then be compared with idealized, de facto unrealizable parallel plate capacitors. This methodology allows overall uncertainty of capacitive sensors to be decomposed into their individual components and sorted in terms of their contribution to the uncertainty budget. The individual FEM-based analysis then enables a systematic analysis of the sources of uncertainty and, thus, reveals possibilities to improve manufacturing processes for capacitive sensors, to put these sensors on a solid metrological basis, and to improve the performance of these displacement measurement systems in the long run, i.e., to provide better sensors for the application.

Контролер тиску для пневматичного гальма на основі «фільтра Калмана»

У рамках роботи було досліджено використання "фільтру Калмана" для управління тиском у пневматичній гальмівній системі в умовах невизначеності та неточності сенсорів, був створений прототип програми на основі математичної моделі фізичної системи та проведено досліди, які підтвердили ефективність даного методуThe study investigated the use of "Kalman filter" for pressure control in the pneumatic brake system while exposed to uncertainty and inaccuracy of sensors, prototype of program based on a mathematical model of the physical system was created. Conducted experiments confirmed the effectiveness of this pressure control method.

A novel TMR based triaxial eddy current test probe for any orientation crack detection

The paper proposes the development, the analysis, and the experimental validation of a novel probe for Eddy Current (EC) Non-Destructive Testing especially thought to warrant good signal to noise ratios in the detection of thin defects when low excitation currents are adopted and whatever the orientation of the probe respect to the orientation of the defect. The probe is based on a simple double coil excitation, that generate a single direction EC flow, and adopts three magnetic sensors that form a triaxial magnetic sensor. Other main contributions of this paper are: i) performance analysis of an EC triaxial probe carried out in simulation environment that considers the measurement uncertainty of the magnetic sensors; ii) an experimental campaign that allow evaluating the performance of the realized probe; iii) a comparison between simulated and experimental results that prove the goodness of the proposed approach.

The Dynamic Calibration Uncertainty of Fast Response Pressure Probes

Abstract In this article, error sources affecting the dynamic calibration of fast-response pressure probes in shock tubes are examined. In particular, the sensors uncertainty, the uncertainty in the rising point of the pressure step, and the nonideality of the step are treated. The latter refers to the presence of pressure oscillations past the shock front, which are particularly important in the case of low-pressure shock tubes, typically used for the calibration of pressure probes for turbomachinery applications. The nonideality effect is investigated using a linear time invariant (LTI) second-order model for the transfer function of the probe’s line-cavity system and an existing analytical model for the post-shock oscillations. The effects of these uncertainty sources to the experimentally determined transfer function of a fast-response probe calibrated in the von Karman Institute (VKI) shock tube are finally presented.

New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery

Data optimization, or optimal experiment design, is an effective way to improve and guarantee the accuracy of state and parameter estimation, as the quality of data has significant impact on the estimation accuracy. Such capability is especially critical for energy systems requiring high reliability. The common practice of data optimization is to design input excitation by maximizing the Fisher information, and hence minimizes the variance of the estimation error. However, such approach suffers from fundamental limitations, including negligence of estimation bias and system uncertainties in measurement, model, and parameter, which severely restrict the applicability and effectiveness of the method. This paper aims at establishing new criteria and a novel framework for data optimization and estimation error quantification to overcome the fundamental limitations. First, a generic formula is derived for quantifying the estimation error subject to sensor, model, and parameter uncertainties for the commonly used least-squares algorithm. Based on the formula, desirable data structures, which could minimize the errors caused by each uncertainty, are identified. These structures are then used as new criteria to formulate the novel data optimization framework. The proposed methodology is applied to the parameter estimation problem of a lithium-ion battery electrochemical model in simulation and experiments, showing up to two orders of magnitude improvement in estimation accuracy compared with the traditional Fisher-information-based approach and other baselines.

Towards Energy-Aware Feedback Planning for Long-Range Autonomous Underwater Vehicles

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Sensor Reduction, Estimation, and Control of an Upper-Limb Exoskeleton

A multi-degree-of-freedom (multi-DOF) exoskeleton relies on an array of sensors to communicate its state (e.g., positions/orientations) and operator-exoskeleton contact interactions (e.g., forces/torques) to its control system. Although sensor redundancy is common in biological systems to cope with uncertainty and partial failure of sensors, in man-made systems, sensor redundancy increases the overall system's cost and control complexity. This study presents a sensor reduction technique for force/torque (F/T) sensors utilizing a Kalman filter-based sensor fusion system in the context of admittance control. The methodology is applied to the EXO-UL8 exoskeleton, which is a powered, redundant, dual-arm, upper-limb robotic system with (7 arm + 1 hand) DOFs incorporating three 6-axis F/T sensors in each arm. Motivated by improving wearability through minimizing human-exoskeleton contact interfaces, which reduces spurious contact forces due to joint misalignment; and reducing cost, the proposed strategy emulates the admittance controller's virtual dynamics with only a subset of sensors, resulting in the physical human-robot interaction feeling the same from the operator's perspective. Experimental results indicate that human-exoskeleton power exchange and actuation stresses of the operator's joints, with the proposed strategy on a subset of two sensors, are comparable to those in the full three-sensor case (p $ 0.01). The experiments verify the proposed methodology for the EXO-UL8, and support the feasibility of operating other Kalman filter-based sensor fusion systems with fewer sensors without sacrificing transparency in physical human-robot interaction.

Deep spectral CNN for laser induced breakdown spectroscopy

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.

The Analysis and Modelling of the Quality of Information Acquired from Weather Station Sensors

This article explores the quality of information acquired from weather station sensors. A review of literature in this field concludes that most publications concern the analysis of data acquired from weather station sensors and their characteristic properties, estimating the missing values from the data, and assessing the quality of weather information. Despite the large collection of studies devoted to these issues, there is no comprehensive approach that would consider the modelling of information uncertainty. Therefore, the article presents a proprietary method of analysing and modelling the uncertainty of the weather station sensors’ information quality. For this purpose, the structure of a real meteorological station and the measurement data obtained from it were analysed. Next, an information quality model was developed using the certainty factor (CF) of hypothesis calculation. The developed method was verified on an exemplary real meteorological station. It was found that this method enables the improvement of the quality of information obtained and processed in a multi-sensor system. This becomes practical when the influence of individual measurement system elements on the information quality reaching the recipient is determined. An example is furnished by a demonstration of the usage of two sensors to improve the information quality.

A Heteroscedastic Likelihood Model for Two-Frame Optical Flow

Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. Vision includes intrinsic uncertainty due to the camera sensor and extrinsic uncertainty due to environmental lighting and texture, which propagate through the image processing algorithms used to produce visual measurements. To faithfully characterise visual measurements, we must take into account these uncertainties. In this letter, we propose a new class of likelihood functions that characterises the uncertainty of the error distribution of two-frame optical flow that enables a heteroscedastic dependence on texture. We employ the proposed class to characterise the Farneback and Lucas Kanade optical flow algorithms and achieve close agreement with their respective empirical error distributions over a wide range of texture in a simulated environment. The utility of the proposed likelihood model is demonstrated in a visual odometry ego-motion study, which results in performance competitive with contemporary methods. The development of an empirically congruent likelihood model advances the requisite tool-set for vision-based Bayesian inference and enables sensor data fusion with GPS, LiDAR and IMU to advance robust autonomous navigation.

Characterization of a Commercial Thin Film-based Pressure Sensor

Aiming at monitoring of lower limb movement, a MEMS integrated chip packaged in a sensing suit and a thin film pressure sensor packaged in a sensing insole are used to collect lower limb motion information. However, it was found that there was a huge error in the measurement of pressure when using the film-based pressure sensors to monitor the foot pressure in the lower limb movement. Hence, this paper describes a method for calibration of soft pressure sensors and characterizes the pressure through the proposed approach. The results show that there is a nonlinear relationship between the input (pressure) and the output (inverse value of the electrical resistance); the fitting error is about 5.5%; the hysteresis is around 4.2%;the zero drift is about 0.11%; the repeatability error is nearly 10.7% and the uncertainty of the sensor is about 14.64%. Therefore, it suggests that there often are notable repeatability errors and sensor uncertainty in the soft thin film pressure sensors, which would affect the accuracy in the monitoring of lower limb movements.

A Grid-Based Framework for Collective Perception in Autonomous Vehicles.

Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion

Incorporating multi-sensor, in filter-based as well as graph-based simultaneous localization and mapping (SLAM), relies on the uncertainties involved in each measurement. Proper covariance estimation is thus critical to balance confidence levels among sensors. Despite its importance, traditional covariance approximation mostly relied on first order derivative or fixed measurement covariance and therefore tended to be error-prone, and even heuristic. Recently, deep learning for uncertainty estimation yielded meaningful performance, but applied to a single sensor in a supervised manner. Unlike the traditional supervised manner, we introduce an unsupervised loss for uncertainty modeling, to learn uncertainty without needing ground truth covariance as a label. Most important, we overcome the limitation of learning a single sensor's uncertainty by introducing a way of balancing uncertainty between different sensor modalities. In doing so, we alleviate the uncertainty balancing issue between sensors that has often been encountered in the multi-sensor SLAM application. Targeting covariance learning for visual odometry, particularly with regard to the integration of inertial sensors, the proposed uncertainty learning method was validated in a visual-inertial odometry application over the public datasets under artificial visual and inertial degradations to mimic harsh environment.

Cool Skin Effect and its Impact on the Computation of the Latent Heat Flux in the South China Sea

AbstractThe cool skin effect, known as the temperature difference (ΔT) across the skin layer of sea surface, is of vital importance for the accurate computation of the latent heat flux (LHF). The observed features of ΔT in the South China Sea are analyzed using in situ data from a buoy platform over an ∼6‐week period. Only nighttime data are used to exclude the possible warm layer effect. The positive values of ΔT falling into the range of 0–1 K comprise 95% of the data, and the most frequently observed values occur in the range of 0.4–0.6 K (38%). The cool skin model in the Coupled Ocean‐Atmosphere Response Experiment 3.0 algorithm is then validated against those observations. The cool skin model has an efficient but insufficient ability to reduce the overestimation of the LHF. The overestimation of the LHF is reduced to 9.5% from 18.0%, leaving nearly half of the biases in the LHF unresolved. The Saunders constant (λ) in the cool skin model is markedly underestimated, leading to a much weaker prediction of ΔT. A strong linear relationship exists between the mean values of λ and the LHF with a slope of −0.9 W m−2. With an approximately doubled λ, the biases in ΔT and in the LHF could be eliminated. Considering the possible uncertainties in sensors, the value of λ is estimated as 11.6 ± 6.7 in the current study.

Experimental path-following of equilibria using Newton’s method. Part I: Theory, modelling, experiments

Modern numerical path-following techniques provide a comprehensive suite of computational tools to study the bifurcation behaviour of engineering structures. In contrast, experimental testing of load-bearing nonlinear structures is still performed using simple force control (dead loading) or displacement control (rigid loading). This means that established experimental methods cannot trace equilibrium manifolds in their entirety because structures snap to alternative equilibria at limit points in the forcing parameter and because branch switching to alternative equilibria cannot be controlled and performed reliably. To extend current testing methods, in Part I of this paper, we implement an experimental path-following method that uses tangent quantities (stiffness and residual forces) and Newton’s method to continue along stable and unstable equilibrium paths and traverse limit points. In addition to enforcing the displacement at primary load-introduction points, the overall shape of the structure is controlled via secondary actuators and sensors. Small perturbations of the structure using the secondary actuators allow an experimental tangent stiffness to be computed, which is then used in a control algorithm. As a pertinent test case, the experimental method is applied to a transversely loaded shallow circular arch. Due to the complexity of the test setup, the experiment is first designed using a virtual testing environment based on a surrogate finite element model. Experimental results demonstrate the robustness of the proposed experimental method and the usefulness of virtual testing as a surrogate, but also highlight that experimental efficiency and the effects of noise and sensor uncertainty is of particular concern. In Part II, we present perspectives on future research directions and novel testing capabilities that are enabled by extending the methodology to pinpointing of critical points, tracing of critical boundaries, and branch switching.

Low-Cost MEMS-Based NARX Model for GPS-Denied Areas

Autonomous vehicle navigation has witnessed a huge revolutionary revision regarding development in Micro-Electro Mechanical System (MEMS) technology. Most recently, Strapdown Inertial Navigation System (SDINS) has successfully been integrated with Global Positioning System (GPS). However, different grades of MEMS inertial sensors are available and choosing the convenient grade is quite important. Noises in inertial sensor are mostly treated through de-noising the additive errors to improve the precision of SDINS output. Unfortunately, integration in SDINS mechanization causes a growing in SDINS error output which considered the main challenge in integrating MEMS inertial sensors with GPS. This paper aims to promote the long-term performance of the MEMS-SDINS/GPS integrated system. A new integrated structure is proposed to model the nonlinearities that exist in SDINS dynamics in addition to the error uncertainty in the inertial sensors’ measurements. A robust Nonlinear AutoRegressive models with eXogenous inputs (NARX) based algorithm are designed for data fusion in the proposed GPS/INS integrated system. Validation for the proposed integrated system has been carried out using different field tests data in order to assess the accuracy of the system during GPS denied environment. The results obtained demonstrate that the proposed NARX model is applicative and satisfactory which shows a desired prediction performance.

A Global Path Planner for Safe Navigation of Autonomous Vehicles in Uncertain Environments.

Autonomous vehicles (AVs) are considered an emerging technology revolution. Planning paths that are safe to drive on contributes greatly to expediting AV adoption. However, the main barrier to this adoption is navigation under sensor uncertainty, with the understanding that there is no perfect sensing solution for all driving environments. In this paper, we propose a global safe path planner that analyzes sensor uncertainty and determines optimal paths. The path planner has two components: sensor analytics and path finder. The sensor analytics component combines the uncertainties of all sensors to evaluate the positioning and navigation performance of an AV at given locations and times. The path finder component then utilizes the acquired sensor performance and creates a weight based on safety for each road segment. The operation and quality of the proposed path finder are demonstrated through simulations. The simulation results reveal that the proposed safe path planner generates paths that significantly improve the navigation safety in complex dynamic environments when compared to the paths generated by conventional approaches.

The UKF Based Approach to Improving Attitude and Position of Quadcopter Through Autonomous and Non-Autonomous Flight

The gyroscope and accelerometer are the basic sensors used by most Unmanned Aerial Vehicle (UAV) like quadcopter to control itself. In this paper, the fault detection of measured angular and linear states by gyroscope and accelerometer sensors are present. Uncertainties in measurement and physical sensors itself are the main reasons that lead to generate noise and cause the fault in measured states. Most previous solutions are process angular or linear states to improving the performance of quadcopter. Also, in most of the previous solutions, KF and EKF filters are used, which are inefficient in dealing with high nonlinearity systems such as quadcopter. The proposed algorithm is developed by the robust nonlinear filter, Unscented Kalman Filter (UKF), as an angular and linear estimation filter. Simulation results show that the proposed algorithm is efficient to decrease the effect of sensors noise and estimate accurate angular and linear states. Also, improving the stability and performance properties of the quadcopter. In addition, the new algorithm leads to increasing the range of nonlinearity movements that quadcopter can perform it.

Propagation of soil moisture sensing uncertainty into estimation of total soil water, evapotranspiration and irrigation decision-making

Soil moisture sensors are subject to uncertainty (inaccuracy) in measuring soil water status, that hinders various applications. User groups (researchers and growers/advisers) rely on these sensors for estimating critical agricultural water management decisions and information such as total soil water in the crop root zone (TSW), crop evapotranspiration (ETc) and predicting irrigation triggers (IT), i.e., when TSW is equal to or lower than readily available water. There is a lack of translation of errors in sensor-reported soil moisture (θv) into TSW, ETc, and IT, which is critical to farm-level decision-making as well as research assessments. Nine soil moisture sensors (based on principles of time-domain reflectometry, capacitance and electrical resistance) were investigated in field conditions for silt loam and loamy sand soils under two installation orientations (vertical and horizontal) during two growing seasons (2017 and 2018). Accurate representation of TSW, ETc, and IT was found to be a function of sensor-type, soil-type as well as calibration-type [factory calibration (F.C.) vs. site-specific calibration (S.S.C.)]. Sensor installation orientation did not affect sensor accuracy. Uncertainties in estimation of TSW, ETc and IT were quantified under each condition of use, and sensors were comparatively ranked for effective selection. It was found that all sensors underestimated ETc in silt loam soil. The deviation of sensor-measured ETc from true ETc ranged from −14 to −31 %, which implies that the choice of sensor under a given soil type impacts the quantification of consumptive use of the soil-vegetation system being monitored. Sensors showed both overstimation and underestimation of ETc in loamy sand soil with deviations of sensor-estimated ETc from true ETc ranging from 14 to −61 %. The S.S.C. resulted in 45 and 17 % improvement in TSW and ETc in silt loam soil, respectively, and 42, 80 and 86 % improvement observed in TSW, IT and ETc in loamy sand soil, respectively. The research findings showed that suitability of soil moisture sensors can differ when different target metrics are used as criteria. These findings emphasize the need for evaluating soil moisture sensors based on practical and application-oriented criteria, in addition to reliance on θv accuracy. To the best of authors’ knowledge, this research is the first to translate traditional θv accuracy assessments into practical and application-oriented criteria and use them to evaluate sensors for these specific applications. Sensor rankings and uncertainty associated with their use presented here will allow diverse users to effectively identify sensors for targeted applications in water management decision-making and research.

A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments

Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost low-precision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements.