Sensor Uncertainty Research Articles

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

An estimation method for vision-based autonomous landing system for fixed wing aircraft

In this paper, we propose a vision-based estimation method for autonomous landing of fixed-wing aircraft for Advanced Air Mobility. The concept of autonomous flight with minimal human intervention has gained significant interest with a particular focus on autonomous landing. The goal is to overcome the limitations of existing instrument landing systems and reduce reliance on GNSS during aircraft landing. The proposed system leverages the following techniques for high-performance and real-time operations in various real-world environments and during all stages of landing. A deep learning segmentation model was directly learned, created, and used for robust runway recognition performance against environmental changes around the runway. Algorithms were designed to adapt to different flight stages considering the varying information offered by image data when the aircraft is in mid-air and on the ground. The relative lateral position from the aircraft to the runway was estimated using bird’s-eye-view conversion and inverse projection techniques instead of conventional perspective-n-point methods for reducing the recognition error occurring from the conversion between 2D image and 3D world. Finally, the mixed-precision technique was used for the segmentation model to improve inference speed and ensure real-time operation in real-world deployment. Estimation stability and reliability were improved by using a Kalman Filter to cope with sensor uncertainties caused by the real-world flight environment. Consequently, we show that the average error in the lateral position estimation by the proposed method is comparable to the accuracy of a single GNSS and demonstrate successful autonomous landing of our test aircraft with a set of flight tests.

Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.

Error analysis of dead reckoning navigation system by considering uncertainties in an underwater vehicle's sensors

Abstract In this paper, a complete introduction to the dead reckoning navigation technique is offered after a discussion of the many forms of navigation, and the benefits and drawbacks associated with each of those types of navigation. After that, the dead reckoning navigation solution is used as an option that is both low-cost and makes use of the sophisticated equations that are used by the system. Moreover, to achieve the highest level of accuracy in navigation, an investigation of navigation errors caused by dead reckoning is calculated. Employing the suggested dead reckoning navigation system, the final position of an underwater vehicle can be established with a high degree of accuracy by using experimental data (from sensors) and the uncertainties that are associated with the system. Finally, to illustrate the correctness of the dead reckoning navigation process, the system error analysis as uncertainty that was carried out using experimental data using the dead reckoning navigation technique is compared with GPS data.

Mitigating Measurement Inaccuracies in Digital Twins of Construction Machinery through Multi-Objective Optimization.

The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design's performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.

Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.

Asymptotic adaptive output-feedback stabilisation of nonlinear systems with unknown growth rate and sensor uncertainty

For a class of nonlinear systems with sensor uncertainty, the problem of adaptive output feedback stabilisation is investigated in this paper. It is assumed that only the output is available for feedback control, which suffers from sensor uncertainty. At the same time, the nonlinear function in the system model has an unknown growth rate. To solve this problem, a set of state observers is designed to estimate the unknown system states based on the uncertain output, and an adaptive output-feedback control is designed based on this. By using the corollary of Barbalet's Lemma, it is shown that the equilibrium of the closed-loop system is asymptotically stable. Finally, simulation results verify the effectiveness of the control scheme

Moving target positioning algorithm based on multidimensional scaling analysis from TDOA and FDOA with sensor uncertainties

Moving target positioning algorithm based on multidimensional scaling analysis from TDOA and FDOA with sensor uncertainties

Uncertainty evaluation for wind speed measurement part (2): “Input quantity correlation”

When the “Guide to the expression of uncertainty in measurement” (GUM) is used to evaluate the uncertainty of wind speed sensors, there is a lack of input quantities correlation research, which leads to the deviation of the uncertainty evaluation result. This paper takes the calibration result of an automatic weather station's three-cup wind speed sensor as an example to evaluate the uncertainty of wind speed sensor measurement based on the correlation of input quantities. First, the calibration point of 10.00 m/s wind speed is taken as an example, and the original wind speed record is obtained by calibrating the three-cup wind speed sensor. Then, according to the GUM evaluation method, the uncertainty of the wind speed measurement result is analyzed and evaluated, And analyze and explore the correlation between the input quantities (considering the influence of the stability and uniformity of the wind tunnel on the measurement result, and the existence of a certain interdependence between them, an experimental plan was developed to test the stability and uniformity of the wind tunnel, and the correlation coefficient between them was calculated to be 0.1561 at the wind speed point of 10.00 m/s). Finally, the input quantities correlation (the correlation coefficient of wind tunnel stability and uniformity) is integrated into the uncertainty evaluation, and the uncertainty evaluation result is obtained at 10.00 m/s wind speed point (where U = 0.22 m/s, U95 = 0.22 m/s). Through comparison, the uncertainty results evaluated without considering the correlation of input quantities have generated negative deviation, with deviation values being −0.00022 m/s. Due to the relatively small deviation, in practical applications, it can be determined whether to correct it based on the accuracy requirements of the measurement result and actual needs.

Strategies for hollow cathode command in short electrodynamics tether-based deorbit devices

Heaterless hollow cathodes are a promising solution for reaching a good cathodic contact in electrodynamic tether systems. However, these cathodes cannot keep the plasma discharge if the electric current IC is below a threshold Imin. If the tether is short, and to save power and expellant, an appropriate command strategy is needed to ensure that the cathode is triggered in conditions satisfying IC>Imin. This study investigates four command strategies, including two fully autonomous approaches (one based on satellite position and the other on local plasma density) and two strategies involving ground segment access, differing in command generation location (ground segment and on-board computer). The success rates of the four strategies were studied though numerical simulation and for the specific scenario of the E.T.PACK demonstration mission. The results demonstrate that, as compared to continuous operation of the hollow cathode, the proposed strategies reduce propellant wastage by a factor between 5 and 10. The strategy requiring ground segment access and with the onboard computer making the command decision has the highest success rate (94%). Among the autonomous configurations, the strategy based solely on local plasma density achieves the highest success rate (69%). Additionally, the impact of sensor accuracy on success rates was assessed via Monte Carlo analysis. The command strategy relying on plasma density measurement is the most sensitivity strategy, with a 10% decrease in success rate due to sensor uncertainty.

Dynamic event-triggered control of large-scale nonlinear systems with sensor uncertainty

In this article, the dynamic event-triggered control problem is investigated for a class of large-scale nonlinear systems subject to sensor uncertainty. The unknown nonlinearities involved in each subsystem are assumed to be bounded by time-varying continuous functions multiplied by unknown constants and unmeasured states. To estimate the unmeasured states of each subsystem, a time-varying high-gain observer is constructed. Then, a dynamic event-triggered mechanism is proposed by introducing an internal dynamic variable in triggering function. Combined with the dual-domination approach, a dynamic event-triggered output feedback controller is developed for each subsystem. Subsequently, it is proved the convergence of all states is ensured based on the Lyapunov theory and the Zeno behavior can be avoided. Eventually, an example is presented to demonstrate the effectiveness of the proposed dynamic event-triggered control scheme.

Оценка требований к точности сенсоров океанографических зондов при косвенном определении солености морской воды

The issues of measuring the mass of dissolved substances or absolute salinity under environmental conditions in situ using automated instruments still remain open. How these issues are likely to be resolved depends on the development of new oceanographic measurement technologies. Salinity is one of the seawater parameters that cannot yet be directly measured in situ. Until now, all known, existing or promising, methods for determining salinity are indirect. The purpose of this work is to find the maximum sensitivity coefficients in order to assess the uncertainty of sensors of input quantities and compare various methods for indirect determination of salinity. To solve this problem, the work uses methods of direct and inverse metrology problems. In this work, these problems were solved for four fundamentally different methods of indirect (calculated) determining of the salinity of sea water: 1) the relative electrical conductivity method (RCM), 2) the speed of sound method (SSM), 3) the density method (DM) and 4) the refractive index method (RIM). Depending on environmental conditions, the maximum values of sensitivity coefficients (13 coefficients in total) for the output value (salinity) were found for all input parameters of each method. By solving inverse problems for the accepted conditional base level of salinity uncertainty and at maximum sensitivity coefficients, estimates of the uncertainty of sensors of input parameters for each of the four methods were calculated. The results obtained make it possible to estimate the required accuracy of sensors of input quantities at any given level of uncertainty of the output quantity or the actual accuracy of the output quantity at any given level of uncertainty of sensors of input quantities. From the three promising methods for indirect salinity determination, MPP is the best candidate to replace MOE in the near future.

Sensor and sensor fusion technology in autonomous vehicles

The perception and navigation of autonomous vehicles heavily rely on the utilization of sensor technology and the integration of sensor fusion techniques, which play an essential role in ensuring a secure and proficient understanding of the vehicle's environment.This paper highlights the significance of sensors in autonomous vehicles and how sensor fusion techniques enhance their capabilities. Firstly, the paper introduces the different types of sensors commonly used in autonomous vehicles and explains their principles of operation, strengths, and limitations in capturing essential information about the vehicles environment. Next, the paper discusses various sensor fusion algorithms, such as Kalman filters and particle filters. Furthermore, the paper explores the challenges associated with sensor fusion and addresses the issue of handling sensor failures or uncertainties. The benefits of sensor fusion technology in autonomous vehicles are also presented. These include improved perception of the environment, enhanced object recognition and tracking, better trajectory planning, and enhanced safety through redundancy and fault tolerance. Lastly, the paper discusses the advancements and highlights the integration of artificial intelligence and machine learning techniques to optimize sensor fusion algorithms and improve the overall autonomy of the vehicle. Following thorough analysis, the deduction can be made that sensor and sensor fusion technology assume a critical function in facilitating efficient and secure autonomous vehicle navigation within intricate surroundings.

Data-driven online tracking filter architecture: A LightGBM implementation

State estimation has been dominated by traditional algorithms based on the Bayesian framework, and Bayesian filtering has proven to be an effective component with a wide range of applications in various fields. On the other hand, Bayesian framework is inherently limited by the precise model prior information, while struggling to cope with the challenges such as Markov assumptions that do not adequately describe the actual motion modes, and model transition decision delay in the multiple model algorithm. To alleviate this, we redefine the state filtering problem from a data-driven perspective as a non-probabilistic mapping problem from measurement sequence to target state, replacing the iteration of model derivation with supervised learning regression. Specifically, we introduce a data-driven tracking filter architecture (DTF) that includes spatio-temporal feature processing and decoupling of target trend and sensor uncertainty features. Its fast implementation based on LightGBM is further given, considering the interpretability of the model. Simulation results show that the proposed method approaches to the theoretical optimal solution of the Kalman filter in an ideal linear Gaussian scenario. In challenging maneuvering target tracking scenarios, the proposed model is superior in performance compared with other existing methods. Finally, we validate the effectiveness of the method in radar tracking and real-world video scenes.

A Particle Fusion Approach for Distributed Filtering and Smoothing

Distributed state estimation is an important tool for coordinated team decision-making and typically involves sharing information between robots in order to outperform individual state estimation. The shared information typically takes the form of relative measurements which allow team members to act as virtual sensors for other robots where the virtual sensor uncertainty is corrupted also by the team member’s state uncertainty. However, incorporating relative measurements commonly depends on a pointwise product operation in most distributed estimation techniques which is well defined for continuously-valued distributions but ill-defined for particle-based distributions. We propose a drop-in replacement for the pointwise product based on using the generalized Hölder’s inequality to upper-bound the product over a series of grid cell sets that discretize the state space. This upper-bound is well defined for particle-based distributions and allows for tighter approximations by decreasing the volume of the sets. We leverage the approach to realize two distributed estimation strategies that use a pointwise product, the Kullback–Leibler Average and Belief Propagation and use these methods in simulations and experiments with a pair of miniature autonomous blimps. We found that after distributed smoothing, we were able to achieve an average improvement of ∼25.1% and ∼35.2% in the position tracking error, indicating our distributed smoothing strategy is able to improve the tracking performance of our initial filtering estimates.

Design Optimization and Measurement Uncertainty of an Electromagnetic Level Sensor for Liquid Metal Reactors

Design Optimization and Measurement Uncertainty of an Electromagnetic Level Sensor for Liquid Metal Reactors

Construction of Probability Graph Optimization Model on Basis of Localization Algorithm

Accurate positioning is crucial in fields such as indoor positioning, autonomous driving, and robot navigation. However, traditional base station positioning algorithms are often affected by factors such as sensor errors, environmental noise, and uncertainty, resulting in certain errors in the positioning results. The article constructed a probability graph model using Radio Frequency Identification (RFID) by using position estimation results as variables. In this model, each position estimation result was represented as a node, and the dependency relationship between positions was represented as an edge. This article observed sensor data and uses Bayesian inference methods to update the probability distribution of each node. The experimental results showed that compared to traditional base station positioning algorithms, the probability graph optimization model based on radio frequency identification can significantly improve the accuracy and reliability of positioning. The highest values for both reached 0.989 and 0.95, respectively. This model has significant advantages in positioning tasks and provides an effective solution for practical applications.

Optimal Pose Estimation and Covariance Analysis with Simultaneous Localization and Mapping Applications

This work provides a theoretical analysis for optimally solving the pose estimation problem using total-least-squares for vector observations from landmark features, which is central to applications involving simultaneous localization and mapping. First, the optimization process is formulated with observation vectors extracted from point-cloud features. Then, error-covariance expressions are derived. The attitude and position estimates obtained via the derived optimization process are proven to reach the bounds defined by the Cramér–Rao lower bound under the small-angle approximation of attitude errors. A fully populated observation noise-covariance matrix is assumed as the weight in the cost function to cover the most general case of the sensor uncertainty. This includes more generic correlations in the errors than previous cases involving an isotropic noise assumption. The proposed solution is verified using Monte Carlo simulations, a Gazebo simulation in a robotics operating system, and an experiment with an actual light detection and ranging sensor to validate the error-covariance analysis.

Generalized Efficiency and the Uncertainty of Millimeter Wave Power Standard

Abstract. In the last decade, waveguide thermoelectric power sensors have been established as an alternative to thermistor mounts for millimeter wave power standards, and due to the detection principle and the nature of waveguides, the generalized efficiency is used as calibration quantity instead of the effective efficiency. In this paper, the generalized efficiency and the measurement uncertainty of waveguide thermoelectric sensors are presented and applied to calibrate other types of power sensors in a measurement set-up for frequency up to 170 GHz. Results for commercially available waveguide power sensors are discussed for the interfaces R 900 and R 1.4k.

A holistic physics-based observer for online flooding and ECSA loss detection in FCEV

This paper addresses the critical need for improving fuel cell electric vehicles (FCEVs) reliability to promote their commercialization. A physics-based observer that combines an Extended Kalman Filter (EKF) with a Sequential Probability Ratio Test (SPRT) has been employed to address two major failure modes in FCEV stacks: flooding and reduction of Electrochemical Active Surface Area (ECSA). Furthermore, the observer estimates the hidden states of a standard automotive airpath system to enable state-feedback control. Therefore, the proposed observer is a novel approach which holistically tackles three coupled problems (i.e., flooding, ECSA loss, and airpath state estimation) which are typically addressed individually. Additionally, the observer is computationally affordable and relies solely on typical vehicle sensors. The observer performance has been validated through simulations, testing various dynamic scenarios in the presence of flooding, ECSA loss failures, and sensors uncertainty. The estimation errors have been confined within a 3% error band for all test cases while the health indicators coherently responded in the presence of faults.