Sensor Bias Research Articles

RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping.

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.

Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration.

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system's observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.

A novel transformer-based few-shot learning method for intelligent fault diagnosis with noisy labels under varying working conditions

Recent years have witnessed the success of Few-shot Learning (FSL) methods in equipment reliability enhancement and fault diagnosis, by virtue of learning from limited data and adapting to new operating conditions. However, due to sensor bias, manual collection, and mislabeling, label noise is inevitably introduced into the dataset, which further reduces the quality of supervised information contained in the few-shot dataset, posing significant challenges for accurate fault diagnosis. In this paper, the problem of Few-shot Fault Diagnosis with Noisy Labels (FFDNL) is studied for the first time, and a novel method named Enhanced Transformer with Asymmetric Loss Function (ETALF) is proposed. ETALF leverages the self-attention mechanism of the transformer to dynamically measure the similarity between fault samples in the support set to enhance the model's robustness against label noise, then naturally aggregates the similar samples into corresponding correct prototypes. Furthermore, an asymmetric loss function is designed, which adaptively assigns the model with larger penalties for incorrect category predictions and smaller penalties for correct category predictions, thereby enhancing fault diagnostic performance through inherent asymmetry. Comprehensive experiments are conducted on two benchmark datasets, and the compared results with representative approaches validate the effectiveness of our proposed ETALF in performing intelligent fault diagnosis using limited and noise-labeled data under varying working conditions, which achieves accuracies of 97.77% and 95.78% with 0.2 noisy-level labels during meta-training and meta-testing on the CWRU and KAIST datasets, respectively.

Calibration method of strapdown inertial measurement unit based on two-axis turntable

Abstract This paper analyzes the causes and effects of error in the Strapdown Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS-IMU), utilizing a sensor calibration method based on a two-axis turntable. By considering deterministic errors such as sensor bias, scale factor, and non-orthogonality of the sensitive axis, an error model was established to assess their impact on measurement accuracy. An expression was provided to represent the relationship between sensor data collection and external input stimuli, storing the error parameters of the accelerometer and the gyroscope separately in parameter matrices. The error parameter matrix between input and output quantities was fitted using the least squares method, and error compensation was completed in conjunction with the error model. By comparing the original data with the error-compensated data, the calibration results were obtained. This improved the consistency of the measurements from the sensitive axes to the stimulus source, reduced the zero bias error of each sensitive axis, and enhanced the positioning accuracy of the Strapdown MEMS-IMU.

Testbeam results of irradiated SiGe BiCMOS monolithic silicon pixel detector without internal gain layer

Samples of the monolithic silicon pixel ASIC prototype produced in 2022 within the framework of the Horizon 2020 MONOLITH ERC Advanced project were irradiated with 70 MeV protons up to a fluence of 1 × 1016 neq/cm2, and then tested using a beam of 120 GeV/c pions. The ASIC contains a matrix of 100 μm pitch hexagonal pixels, read out by low noise and very fast frontend electronics produced in a 130 nm SiGe BiCMOS technology process. The dependence on the proton fluence of the efficiency and the time resolution of this prototype was measured with the frontend electronics operated at a power density between 0.13 and 0.9 W/cm2. The testbeam data show that the detection efficiency of 99.96% measured at sensor bias voltage of 200 V before irradiation becomes 96.2% after a fluence of 1 × 1016 neq/cm2. An increase of the sensor bias voltage to 300 V provides an efficiency to 99.7% at that proton fluence. The timing resolution of 20 ps measured before irradiation rises for a proton fluence of 1 × 1016 neq/cm2 to 53 and 45 ps at HV = 200 and 300 V, respectively.

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Abstract Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.

A Lightweight Algorithm to Model Radiation Damage Effects in Monte Carlo Events for High-Luminosity Large Hadron Collider Experiments.

Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect; adjusting sensor bias voltage and detection thresholds can help mitigate these effects, generating simulated data that accurately mirror the performance evolution with the accumulation of luminosity, hence fluence, is crucial. The ATLAS and CMS collaborations have developed and implemented algorithms to correct simulated Monte Carlo (MC) events for radiation damage effects, achieving impressive agreement between collision data and simulated events. In preparation for the high-luminosity phase (HL-LHC), the demand for a faster ATLAS MC production algorithm becomes imperative due to escalating collision, events, tracks, and particle hit rates, imposing stringent constraints on available computing resources. This article outlines the philosophy behind the new algorithm, its implementation strategy, and the essential components involved. The results from closure tests indicate that the events simulated using the new algorithm agree with fully simulated events at the level of few %. The first tests on computing performance show that the new algorithm is as fast as it is when no radiation damage corrections are applied.

Efficient Estimation for Sensor Biases and Target States in the Presence of Sensor Position Errors

Efficient Estimation for Sensor Biases and Target States in the Presence of Sensor Position Errors

Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe

Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challenges persist in reducing uncertainties in satellite remote sensing. Our study examines retrieval biases in MODIS sensors on Terra and Aqua satellites compared to AERONET ground-based measurements. We assess their performance and the correlation with the AERONET aerosol optical depth (AOD) using 14 years of data (2010–2023) from 29 AERONET stations across 10 Central–East European countries. The results indicate discrepancies between MODIS Terra and Aqua retrievals: Terra overestimates the AOD at 16 AERONET stations, while Aqua underestimates the AOD at 21 stations. The examination of temporal biases in the AOD using the calculated estimated error (ER) between AERONET and MODIS retrievals reveals a notable seasonality in coincident retrievals. Both sensors show higher positive AOD biases against AERONET in spring and summer compared to fall and winter, with few ER values for Aqua indicating poor agreement with AERONET. Seasonal variations in correlation strength were noted, with significant improvements from winter to summer (from R2 of 0.58 in winter to R2 of 0.76 in summer for MODIS Terra and from R2 of 0.53 in winter to R2 of 0.74 in summer for MODIS Aqua). Over the fourteen-year period, monthly mean aerosol AOD trends indicate a decrease of −0.00027 from AERONET retrievals and negative monthly mean trends of the AOD from collocated MODIS Terra and Aqua retrievals of −0.00023 and −0.00025, respectively. An aerosol classification analysis showed that mixed aerosols comprised over 30% of the total aerosol composition, while polluted aerosols accounted for more than 22%, and continental aerosols contributed between 22% and 24%. The remaining 20% consists of biomass-burning, dust, and marine aerosols. Based on the aerosol classification method, we computed the bias between the AERONET AE and MODIS AE, which showed higher AE values for AERONET retrievals for a mixture of aerosols and biomass burning, while for marine aerosols, the MODIS AE was larger and for dust the results were inconclusive.

VR interactive input system based on INS and binocular vision fusion

In virtual reality interactive input systems, real-time and accurate spatial position and pose measurement is key to achieving a natural user interface. This study explores the application of inertial measurement units and binocular vision fusion technology in virtual reality interactive input systems, with the aim of improving the tracking accuracy of the system through optimized pose models and visual algorithms. A virtual reality measurement technology that integrates inertial measurement units and binocular vision is proposed by using an improved Kalman filtering algorithm to process inertial measurement units data, and combining it with the SURF algorithm optimized binocular vision system. Experimental results showed that fusion technology could reduce sensor noise and bias, improve the accuracy of pose estimation, and promote stable and robust motion tracking in virtual reality systems. In the angle range of -90 ° to 90 °, the average absolute error of the fusion system compared to the simple inertial measurement units pose calculation decreased from 3.371 ° to 1.369 ° In the experiment of measuring distance with a binocular vision system, the average absolute error value decreased to 1.532 mm, and the error range of the marker within the distance range of 500 mm to 650 mm was controlled within -1.3 mm to 1.2 mm. This study provides an effective solution for achieving high-precision virtual reality interactive input systems and is meaningful for the advancement of virtual reality technology.

Monitoring of carbon ion therapeutic beams with thin silicon sensors

Single ion counting in particle therapy may lead to new beam monitoring systems, enabling innovative delivery strategies that are faster and more sensitive than those currently used in clinics. Previous studies carried out by the University and the National Institute of Nuclear Physics (INFN) of Turin have demonstrated the feasibility of using thin silicon detectors to count single protons in clinical beams. The aim of this work is to report the performance of a strip-segmented 60-μm thick silicon PIN sensor used for single carbon ion discrimination. All measurements were performed using the CNAO synchrotron at different beam energies covering the clinical energy range (115–399 MeV/u). Signals from the sensor strips were read using a custom amplifier board and sampled with a 5 GS/s digitizer. The carbon ion signals were analyzed in terms of amplitude, duration, and deposited charge at different sensor bias voltages.

A mobile prototype-based localization approach using inertial navigation and acoustic tracking for underwater

During underwater operations, divers must determine their own trajectories using the Inertial Navigation System (INS) they carry to improve operational efficiency. However, the INS contains a sensor bias that is also incorporated into the quadratic integration process to obtain the displacement, resulting in trajectory drift of the divers during prolonged self-guidance. To overcome the above problem, other aids are needed to correct the accumulated error of the INS. The single-beacon Assisted Inertial Navigation (AIN) method can improve the flexibility of inertial error correction while simplifying the localization equipment, which is suitable for the INS cumulative error correction scenario of divers. However, most of the traditional single-beacon assisted correction methods do not consider the effect of acoustic line bending on hydroacoustic ranging, and at the same time, they do not consider the problem of singular or pathological coefficient matrices introduced by inertial navigation neighbor localization deviations. Based on the above two shortcomings, this paper uses the acoustic velocity profile for acoustic line tracking, combines the localization idea of Mobile Primitives (MP), and proposes an MP-based acoustic line tracking-Assisted Inertial Navigation Localization (AINL) method, which constructs a sliding time window (STW) by taking the historical positioning of divers as a virtual primitive, and combines the nonlinear optimization method for iterative optimization search as a means to improve the accuracy and stability of self-navigation of the divers.

Variable structure bidimensional reference pattern-based anti-bias track to track association

Aiming at distributed sensors with nonlinear biases, this paper proposes an anti-bias track-to-track association algorithm named variable structure bidimensional reference pattern (VSBRP). Firstly, we theoretically analyze the limitations of the original reference-pattern (REP) in dealing with sensor nonlinear bias. Subsequently, to eliminate the adverse effects of sensor bias on track-to-track association, we reformulate the reference-pattern feature by organizing the position and velocity information, with the aim of estimating the relative bias using multiple dimensions of information. Additionally, we propose to regulate the structure of REP in real-time according to missed and false tracks, thereby improving the robustness of the target tracking system. Numerical simulations are performed to demonstrate the effectiveness and superiority of the proposed method.

Gaussian Mixture Probability Hypothesis Density Filter for Heterogeneous Multi-Sensor Registration

Spatial registration is a prerequisite for data fusion. Existing methods primarily focus on similar sensor scenarios and rely on accurate data association assumptions. To address the heterogeneous sensor registration in complex data association scenarios, this paper proposes a Gaussian mixture probability hypothesis density (GM-PHD)-based algorithm for heterogeneous sensor bias registration, accompanied by an adaptive measurement iterative update algorithm. Firstly, by constructing augmented target state motion and measurement models, a closed-form expression for prediction is derived based on Gaussian mixture (GM). In the subsequent update, a two-level Kalman filter is used to achieve an approximate decoupled estimation of the target state and measurement bias, taking into account the coupling between them through pseudo-likelihood. Notably, for heterogeneous sensors that cannot directly use sequential update techniques, sequential updates are first performed on sensors that can obtain complete measurements, followed by filtering updates using extended Kalman filter (EKF) sequential update techniques for incomplete measurements. When there are differences in sensor quality, the GM-PHD fusion filter based on measurement iteration update is sequence-sensitive. Therefore, the optimal subpattern assignment (OSPA) metric is used to optimize the fusion order and enhance registration performance. The proposed algorithms extend the multi-target information-based spatial registration algorithm to heterogeneous sensor scenarios and address the impact of different sensor-filtering orders on registration performance. Our proposed algorithms significantly improve the accuracy of bias estimation compared to the registration algorithm based on significant targets. Under different detection probabilities and clutter intensities, the average root mean square error (RMSE) of distance and angular biases decreased by 11.8% and 8.6%, respectively.

Test measurements of an ASIC for X-ray material discrimination by using on-chip time domain integration and a CdTe sensor

Using time-domain integration (TDI) and a two-dimensional sensor is beneficial for X-ray imaging of moving objects. Applying on-chip instead off-chip TDI decreases the required data throughput between an ASIC and the backend several times [1]. This, in turn, allows us to significantly simplify the ASIC itself as well as the backend.We present the results of test measurements of an ASIC dedicated for X-ray material discrimination by using on-chip TDI and a CdTe sensor.The main part of the ASIC is an 192 × 64 pixel matrix. The pixel size is 100 μm × 100 μm, so the chip size is approximately 6.7 mm × 2 cm. The chip is manufactured in a 130 nm CMOS technology with 8 metal layers. A single pixel analog front-end consists of a charge-sensitive amplifier, a shaper, and three discriminators followed by counters. The test was conducted with a 0.75 mm thick CdTe sensor.First, we show the results of the discriminator offset correction followed by the evaluation of the pixel analog front-end gain and noise conducted with single energy radiation. Next, the results of moving objects imaging are obtained by using an industrial X-ray machine for food inspection (continuous energy spectrum). This is carried out to present an example of the image and evaluate the image signal to noise ratio for different energy discriminator thresholds, sensor bias voltages, detector module temperatures and object speeds.

MEMS strapdown inertial attitude measurement system using rotational modulation technology.

Attitude determination involves the integration of methodologies and systems for estimating the time varying attitude of moving objects. Strapdown Inertial Attitude Measurement System (SIAMS) is among the most widely used navigation systems. The development of cost effective Micro Electro Mechanic System (MEMS) based inertial sensors has made attitude measurement system more affordable. However, MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable attitude results. Given the auto-compensation of inertial sensor bias in rotation error modulation, the objective of this paper is to develop a MEMS-based rotary SIAMS, in which the significant sensor bias is automatically compensated by rotating the IMU, to offer comparable performance with respect to a tactical-grade Inertial Measurement Unit (IMU). With the analysis of the relationship between the MEMS error and misalignment, a MEMS calibration model is derived, and a combined calibration method of multi position rotation is applied to estimate the deterministic sensor errors such as bias, scale factor, and misalignment. Simulation and experiment results indicate that the proposed method can further modulate and compensate the MEMS errors, thereby improving the MEMS attitude accuracy.

Sensor Fault Detection and Diagnosis of Linear Parabolic PDE Systems With Unknown Inputs

This paper investigates the sensor bias fault detection and diagnosis problem for linear parabolic partial differential equation (PDE) systems under the existence of unknown input signals. A variation of Wirtinger's inequality is used to design a Luenberger-type PDE observer and radial basis function (RBF) neural networks are applied to approximate the unknown inputs, guaranteeing the boundedness of the state estimation error. Taking the measurement error as the fault indication signal, a residual evaluation logic is developed to achieve fault detection. An adaptive diagnostic law is constructed to estimate the values of sensor bias faults once they are detected. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula> -modification RBF neural networks are designed to compensate for the unknown inputs in the presence of sensor faults, ensuring that both state and sensor bias faults estimation errors converge to some adjustable sets. The effectiveness and applicability of the developed fault diagnosis strategy are demonstrated by simulation results on a heat diffusion process.

Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

In this article, a novel approach is presented for drift‐aware feature learning aimed at calibrating drift biases in soft sensors for long‐term use. The proposed method leverages an autoencoder for data preprocessing to extract expressive signal drift traces features, and incorporates drift characteristics through the latent space representation in a long short‐term memory (LSTM) regression neural network. In the results, it is demonstrated that the proposed approach outperforms other typical recurrent neural networks, such as LSTM, gated recurrent unit, and bidirectional LSTM, with a reduced root mean square error of 60% for the training dataset (≈2.5 h) and 80% for the testing dataset (≈20 h). The proposed approach has the potential to optimize the performance of soft sensors with long‐term drift and reduce the need for frequent recalibration. By compensating for sensor drift using existing prior information and limited time data, the proposed neural network can effectively reduce the complexity and computational burden of the system, without the need for additional settings or hyperparameter fine‐tuning.

Radiation tolerance of SiGe BiCMOS monolithic silicon pixel detectors without internal gain layer

A monolithic silicon pixel prototype produced for the MONOLITH ERC Advanced project was irradiated with 70 MeV protons up to a fluence of 1 × 1016 1 MeV n eq/cm2. The ASIC contains a matrix of hexagonal pixels with 100 μm pitch, readout by low-noise and very fast SiGe HBT frontend electronics. Wafers with 50 μm thick epilayer with a resistivity of 350 Ωcm were used to produce a fully depleted sensor. Laboratory tests conducted with a 90Sr source show that the detector works satisfactorily after irradiation. The signal-to-noise ratio is not seen to change up to fluence of 6 × 1014 n eq/cm2. The signal time jitter was estimated as the ratio between the voltage noise and the signal slope at threshold. At -35°C, sensor bias voltage of 200 V and frontend power consumption of 0.9 W/cm2, the time jitter of the most-probable signal amplitude was estimated to be σ t 90Sr = 21 ps for proton fluence up to 6 × 1014 n eq/cm2 and 57 ps at 1 × 1016 n eq/cm2. Increasing the sensor bias to 250 V and the analog voltage of the preamplifier from 1.8 to 2.0 V provides a time jitter of 40 ps at 1 × 1016 n eq/cm2.

Fault Detection Statistics in the Presence of Additive Measurement Errors

In the study, the noncentral Wishart matrix trace-based fault detection statistics is proposed for sensor/actuator fault detection in the presence of measurement bias. As the difference from the most of existing innovation-based fault detection methods, this approach allows to detect sensor/actuator faults in the presence of additive measurement errors. The trace of the noncentral Wishart matrix is used in this method for the fault detection statistics. The proposed innovation approach-based sensor/actuator fault detection using trace of the noncentral Wishart matrix is applied to a dynamic model of an unmanned aerial vehicle (UAV). The sensor bias and actuator loss of control effectiveness type faults are considered. The proposed and traditional methods for detecting faults in the presence of slowly developing gyroscope drift are considered and compared.