Search

The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety–distance–error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety–distance–error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms. [ABSTRACT FROM AUTHOR]

Tactile servoing is an important technique because it enables robots to manipulate objects with precision and accuracy while adapting to changes in their environments in real-time. One approach for tactile servo control with high-resolution soft tactile sensors is to estimate the contact pose relative to an object surface using a convolutional neural network (CNN) for use as a feedback signal. In this paper, we investigate how the surface pose estimation model can be extended to include shear, and utilise these combined pose-and-shear models to develop a tactile robotic system that can be programmed for diverse non-prehensile manipulation tasks, such as object tracking, surface-following, single-arm object pushing and dual-arm object pushing. In doing this, two technical challenges had to be overcome. Firstly, the use of tactile data that includes shear-induced slippage can lead to error-prone estimates unsuitable for accurate control, and so we modified the CNN into a Gaussian-density neural network and used a discriminative Bayesian filter to improve the predictions with a state dynamics model that utilises the robot kinematics. Secondly, to achieve smooth robot motion in 3D space while interacting with objects, we used SE (3) velocity-based servo control, which required re-deriving the Bayesian filter update equations using Lie group theory, as many standard assumptions do not hold for state variables defined on non-Euclidean manifolds. In future, we believe that pose-and-shear-based tactile servoing will enable many object manipulation tasks and the fully-dexterous utilisation of multi-fingered tactile robot hands. [ABSTRACT FROM AUTHOR]

Understanding and predicting novel diseases have become very important owing to the huge global health burden. The organization and study of mathematical models are critical in predicting disease behavior of the disease. In this paper, a new stochastic Susceptible‐Infected‐Recovered‐Death (SIRD) model for spreading epidemic disease is investigated. First, the deterministic SIRD model is considered, and then, by allowing randomness in the recovery and death rates that are not deterministic, the system of nonlinear stochastic differential equations is derived. For the suggested model, the existence and uniqueness of a positive global solution are demonstrated. The parameter estimation is done with the conditional least square estimator for deterministic models and the maximum likelihood estimator for stochastic ones. After that, we investigate a nonadditive state‐space model for spreading epidemic disease by considering infected as the hidden process variable. The problem of the hidden process variable from noisy observations is filtered, predicted, and smoothed using a recursive Bayesian technique. For estimating the hidden number of infected variables, closed‐form solutions are obtained. Finally, numerical simulations with both simulated and real data are performed to demonstrate the efficiency and accuracy of the current work. [ABSTRACT FROM AUTHOR]

Induction machines have become the standard for highly demanding industrial applications. This has led to the utilization of modern discrete-time control techniques (such as model predictive control) that require the estimation of internal variables that are not subject to measurement (such as the rotational velocity in sensorless applications). From this point of view, it is fundamental to have accurate discrete-time models of induction machines, particularly given their nonlinear nature, so that control techniques perform according to design requirements. In spite of the above, the modeling of induction machines has not received much attention in the literature, even though more powerful machines and faster microcontrollers are currently being used. To better understand induction machine models for control, in this paper, we develop and compare various discrete-time models of induction machines based on Euler, Taylor, and Runge–Kutta methods. In addition, we compare the Extended Kalman Filter and Unscented Kalman Filter for state estimation in terms of accuracy and computational burden. The models are derived and compared through extensive Monte Carlo simulations and the state estimation techniques are compared in terms of root mean squared error, execution time, and maximum absolute error. Our simulations show that, in general, the Taylor method yields more accurate models than both the Runge–Kutta and Euler methods. In particular, the Taylor method results in a root mean square error that is one order of magnitude smaller than the Euler method for stator current and rotor flux linkages. For rotor angular speed, the Runge–Kutta methods are more accurate than both the Taylor and Euler methods, resulting in a root mean square error that is two orders of magnitude smaller than the Euler method. On the other hand, the Extended Kalman Filter results in smaller execution time than the Unscented Kalman Filter, up to two orders of magnitude. In terms of root mean squared error and maximum absolute error, both filtering algorithms perform similarly. [ABSTRACT FROM AUTHOR]

This paper presents the application of nonlinear Bayesian filtering–based system identification (SI) methods when employed to estimate the parameters of stochastic fractional dynamic systems. The objective is to demonstrate the capabilities and limitations of time-domain stochastic filtering–based SI for systems endowed with fractional derivative elements when the estimation is performed under different operating conditions. The conditions include measured forcing inputs (input-output identification), stochastic/unmeasured forcing inputs (output-only identification), and different types of measurements and levels of measurement noise, in the context of both linear and hysteretic fractional oscillators. The accuracy and estimation error of three methods was studied, namely, the unscented Kalman filter, the ensemble Kalman filter, and the particle filter. Baseline results that can be applied to the modeling, identification, and control of fractional structural and mechanical systems are provided. It is shown that nonlinear Bayesian filtering methods have the capability to accurately estimate the response and parameters of fractional oscillators, and that the coefficient and order of fractional elements are observable/identifiable from output response measurements. [ABSTRACT FROM AUTHOR]

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

Abstract We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments.

We propose a robust Bayesian filtering framework for state and multi-modal uncertainty estimation in urban settings by fusing diverse sensor measurements. Our framework addresses multi-modal uncertainty from various error sources by tracking a separate probability distribution for linearization points corresponding to dynamics, measurements, and cost functions. Multiple parallel robust Extended Kalman filters (R-EKF) leverage these linearization points to characterize the state probability distribution. Employing Rao–Blackwellization, we combine the linearization point distribution with the state distribution, resulting in a unified, efficient, and outlier-resistant Bayesian filter that captures multi-modal uncertainty. Furthermore, we introduce a gradient descent-based optimization method to refine the filter parameters using available data. Evaluating our filter on real-world data from a multi-sensor setup comprising camera, Global Navigation Satellite System (GNSS), and Attitude and Heading Reference System (AHRS) demonstrates improved performance in bounding position errors based on uncertainty, while maintaining competitive accuracy and comparable computation to existing methods. Our results suggest that our framework is a promising direction for safe and reliable localization in urban environments. [ABSTRACT FROM AUTHOR]

This paper proposes a class of algorithms for analyzing event count time series, based on state space modeling and Kalman filtering. While the dynamics of the state space model is kept Gaussian and linear, a nonlinear observation function is chosen. In order to estimate the states, an iterated extended Kalman filter is employed. Positive definiteness of covariance matrices is preserved by a square-root filtering approach, based on singular value decomposition. Non-negativity of the count data is ensured, either by an exponential observation function, or by a newly introduced "affinely distorted hyperbolic" observation function. The resulting algorithm is applied to time series of the daily number of seizures of drug-resistant epilepsy patients. This number may depend on dosages of simultaneously administered anti-epileptic drugs, their superposition effects, delay effects, and unknown factors, making the objective analysis of seizure counts time series arduous. For the purpose of validation, a simulation study is performed. The results of the time series analysis by state space modeling, using the dosages of the anti-epileptic drugs as external control inputs, provide a decision on the effect of the drugs in a particular patient, with respect to reducing or increasing the number of seizures. [ABSTRACT FROM AUTHOR]

Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual‐level data. A variety of implementations exist for such models, including integrated likelihood, parameter‐expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources. [ABSTRACT FROM AUTHOR]

In wearable sensing, accurate estimation of physiological parameters is paramount, although these signals can be corrupted by noise. The fusion of data from multiple sensor sources has the potential to enhance accuracy, even in the presence of disruptive noise. This paper aims to introduce and compare various existing state-of-the-art and novel data fusion techniques to improve the reliability of heart rate estimation. The comparisons were implemented using the MIT-BIH Arrhythmia database with additive noise signals taken from MIT Noise Stress Test Database. When it comes to the challenging low signal-to-noise ratio (SNR) regions, the Kalman fusion and the α -trim mean filtering approach exhibits the best performance. The Kalman fusion approach dominates when both channels are corrupted, while the α -trim mean filtering elimination algorithm takes the lead when at least one channel is clean. To make the most of these strengths, we have developed an innovative algorithm that can switch between the two fusion methods based on a signal quality indicator (SQI) that serves as a surrogate SNR. This algorithm outperforms the baseline 2-channel RR-interval averaging approach by ≃ 54 % and ≃ 21 % at SNRs of 20 dB and − 20 dB respectively. Moreover, it outperforms other cutting-edge heart rate estimation methods. • Investigation of fusion methods for heart rate estimation from ECG signals. • Signal quality aware fusion algorithm selection. • Fusion methods analyzed at varying input signal-to-noise ratios. • Signal quality index proposed as a surrogate for input signal-to-noise ratio. • Fusion selection method proposed for varying input signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Summary: A symbolic‐numeric method is proposed for addressing the Bayesian filtering problems of a class of discrete‐time nonlinear stochastic systems. We first approximate the posterior probability density function to be Gaussian. The update law of the mean and variance is formulated as the evaluation of several integrals depending on certain parameters. Unlike existing methods, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF), this formulation considers the nonlinearity of system dynamics exactly. To evaluate the integrals efficiently, we introduce an integral transform motivated by the moment generating function (MGF), which we call a quasi MGF. Furthermore, the quasi MGF is compatible with the Fourier transform of differential operators. We utilize this compatibility to decrease the number of computations of Gröbner bases in the noncommutative rings of differential operators, which reduces the offline computational time. A numerical example is presented to show the efficiency of the proposed method compared to that of other existing methods such as the EKF, UKF, and PF. [ABSTRACT FROM AUTHOR]

Since manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose an algorithm for corrosion detection and three algorithms for corrosion prognosis by using Bayesian filtering approaches, and quantitatively compare their accuracy against synthetic datasets having characteristics typical for wall thickness measurements using ultrasound sensors. We found that a corrosion prognosis algorithm based on the Pourbaix corrosion model using unscented Kalman filtering outperforms the algorithms based on a linear corrosion model and the bimodal corrosion model introduced by Melchers.

Induction machines have become the standard for highly demanding industrial applications. This has led to the utilization of modern discrete-time control techniques (such as model predictive control) that require the estimation of internal variables that are not subject to measurement (such as the rotational velocity in sensorless applications). From this point of view, it is fundamental to have accurate discrete-time models of induction machines, particularly given their nonlinear nature, so that control techniques perform according to design requirements. In spite of the above, the modeling of induction machines has not received much attention in the literature, even though more powerful machines and faster microcontrollers are currently being used. To better understand induction machine models for control, in this paper, we develop and compare various discrete-time models of induction machines based on Euler, Taylor, and Runge–Kutta methods. In addition, we compare the Extended Kalman Filter and Unscented Kalman Filter for state estimation in terms of accuracy and computational burden. The models are derived and compared through extensive Monte Carlo simulations and the state estimation techniques are compared in terms of root mean squared error, execution time, and maximum absolute error. Our simulations show that, in general, the Taylor method yields more accurate models than both the Runge–Kutta and Euler methods. In particular, the Taylor method results in a root mean square error that is one order of magnitude smaller than the Euler method for stator current and rotor flux linkages. For rotor angular speed, the Runge–Kutta methods are more accurate than both the Taylor and Euler methods, resulting in a root mean square error that is two orders of magnitude smaller than the Euler method. On the other hand, the Extended Kalman Filter results in smaller execution time than the Unscented Kalman Filter, up to two orders of magnitude. In terms of root mean squared error and maximum absolute error, both filtering algorithms perform similarly.

Face frontalization consists of synthesizing a frontal view from a profile one. This paper proposes a frontalization method that preserves non-rigid facial deformations, i.e. facial expressions. It is shown that expression-preserving frontalization boosts the performance of visually assisted speech processing. The method alternates between the estimation of (i) the rigid transformation (scale, rotation, and translation) and (ii) the non-rigid deformation between an arbitrarily-viewed face and a face model. The method has two important merits: it can deal with non-Gaussian errors in the data and it incorporates a dynamical face deformation model. For that purpose, we use the Student's t-distribution in combination with a Bayesian filter in order to account for both rigid head motions and time-varying facial deformations, e.g. caused by speech production. The zero-mean normalized cross-correlation score is used to evaluate the ability of the method to preserve facial expressions. The method is thoroughly evaluated and compared with several state of the art methods, either based on traditional geometric models or on deep learning. Moreover, we show that the method, when incorporated into speech processing pipelines, improves word recognition rates and speech intelligibility scores by a considerable margin. [ABSTRACT FROM AUTHOR]

A key need in cognitive training interventions is to personalize task difficulty to each user and to adapt this difficulty to continually apply appropriate challenges as users improve their skill to perform the tasks. Here we examine how Bayesian filtering approaches, such as hidden Markov models and Kalman filters, and deep-learning approaches, such as the long short-term memory (LSTM) model, may be useful methods to estimate user skill level and predict appropriate task challenges. A possible advantage of these models over commonly used adaptive methods, such as staircases or blockwise adjustment methods that are based only upon recent performance, is that Bayesian filtering and deep learning approaches can model the trajectory of user performance across multiple sessions and incorporate data from multiple users to optimize local estimates. As a proof of concept, we fit data from two large cohorts of undergraduate students performing WM training using an N-back task. Results show that all three models predict appropriate challenges for different users. However, the hidden Markov models were most accurate in predicting participants' performances as a function of provided challenges, and thus, they placed participants at appropriate future challenges. These data provide good support for the potential of machine learning approaches as appropriate methods to personalize task performance to users in tasks that require adaptively determined challenges.

Since manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose an algorithm for corrosion detection and three algorithms for corrosion prognosis by using Bayesian filtering approaches, and quantitatively compare their accuracy against synthetic datasets having characteristics typical for wall thickness measurements using ultrasound sensors. We found that a corrosion prognosis algorithm based on the Pourbaix corrosion model using unscented Kalman filtering outperforms the algorithms based on a linear corrosion model and the bimodal corrosion model introduced by Melchers. [ABSTRACT FROM AUTHOR]

Real-time satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena such as floods, earthquakes, etc. One important constraint of satellite imaging is the trade-off between spatial/spectral resolution and their revisiting time, a consequence of design and physical constraints imposed by satellite orbit among other technical limitations. In this paper, we focus on fusing multi-temporal, multi-spectral images where data acquired from different instruments with different spatial resolutions is used. We leverage the spatial relationship between images at multiple modalities to generate high-resolution image sequences at higher revisiting rates. To achieve this goal, we formulate the fusion method as a recursive state estimation problem and study its performance in filtering and smoothing contexts. Furthermore, a calibration strategy is proposed to estimate the time-varying temporal dynamics of the image sequence using only a small amount of historical image data. Differently from the training process in traditional machine learning algorithms, which usually require large datasets and computation times, the parameters of the temporal dynamical model are calibrated based on an analytical expression that uses only two of the images in the historical dataset. A distributed version of the Bayesian filtering and smoothing strategies is also proposed to reduce its computational complexity. To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial–temporal resolution image estimates. Our experiments show that the proposed methodology outperforms the competing methods in both estimation accuracy and water mapping tasks. [ABSTRACT FROM AUTHOR]

Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters. In this context, on-line (recursive) Bayesian filtering algorithms typically fail to properly quantify the full posterior uncertainty of timeinvariant model parameters. Off-line (batch) algorithms are--in principle--better suited for the uncertainty quantification task, yet they are computationally prohibitive in sequential settings. In this work, we adapt and investigate selected Bayesian filters for parameter estimation: an on-line particle filter, an on-line iterated batch importance sampling filter, which performs Markov Chain Monte Carlo (MCMC) move steps, and an off-line MCMC-based sequential Monte Carlo filter. A Gaussian mixture model approximates the posterior distribution within the resampling process in all three filters. Two numerical examples provide the basis for a comparative assessment. The first example considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential monitoring measurements. The second high-dimensional, linear, Gaussian example employs a random field to model corrosion deterioration across a beam, which is updated with sequential sensor measurements. The numerical investigations provide insights into the performance of off-line and on-line filters in terms of the accuracy of posterior estimates and the computational cost, when applied to problems of different nature, increasing dimensionality and varying sensor information amount. Importantly, they show that a tailored implementation of the on-line particle filter proves competitive with the computationally demanding MCMC-based filters. Suggestions on the choice of the appropriate method in function of problem characteristics are provided. [ABSTRACT FROM AUTHOR]

Data-informed predictive maintenance planning largely relies on stochastic deterioration models. Monitoring information can be utilized to update sequentially the knowledge on model parameters. In this context, on-line (recursive) Bayesian filtering algorithms typically fail to properly quantify the full posterior uncertainty of time-invariant model parameters. Off-line (batch) algorithms are—in principle—better suited for the uncertainty quantification task, yet they are computationally prohibitive in sequential settings. In this work, we adapt and investigate selected Bayesian filters for parameter estimation: an on-line particle filter, an on-line iterated batch importance sampling filter, which performs Markov Chain Monte Carlo (MCMC) move steps, and an off-line MCMC-based sequential Monte Carlo filter. A Gaussian mixture model approximates the posterior distribution within the resampling process in all three filters. Two numerical examples provide the basis for a comparative assessment. The first example considers a low-dimensional, nonlinear, non-Gaussian probabilistic fatigue crack growth model that is updated with sequential monitoring measurements. The second high-dimensional, linear, Gaussian example employs a random field to model corrosion deterioration across a beam, which is updated with sequential sensor measurements. The numerical investigations provide insights into the performance of off-line and on-line filters in terms of the accuracy of posterior estimates and the computational cost, when applied to problems of different nature, increasing dimensionality and varying sensor information amount. Importantly, they show that a tailored implementation of the on-line particle filter proves competitive with the computationally demanding MCMC-based filters. Suggestions on the choice of the appropriate method in function of problem characteristics are provided.

We propose a new linear RGB-D simultaneous localization and mapping (SLAM) formulation by utilizing planar features of the structured environments. The key idea is to understand a given structured scene and exploit its structural regularities such as the Manhattan world. This understanding allows us to decouple the camera rotation by tracking structural regularities, which makes SLAM problems free from being highly nonlinear. Additionally, it provides a simple yet effective cue for representing planar features, which leads to a linear SLAM formulation. Given an accurate camera rotation, we jointly estimate the camera translation and planar landmarks in the global planar map using a linear Kalman filter. Our linear SLAM method, called L-SLAM, can understand not only the Manhattan world but the more general scenario of the Atlanta world, which consists of a vertical direction and a set of horizontal directions orthogonal to the vertical direction. To this end, we introduce a novel tracking-by-detection scheme that infers the underlying scene structure by Atlanta representation. With efficient Atlanta representation, we formulate a unified linear SLAM framework for structured environments. We evaluate L-SLAM on a synthetic dataset and RGB-D benchmarks, demonstrating comparable performance to other state-of-the-art SLAM methods without using expensive nonlinear optimization. We assess the accuracy of L-SLAM on a practical application of augmented reality. [ABSTRACT FROM AUTHOR]

Multiple motion estimators such as an interacting multiple model (IMM) have been utilized to track target objects such as cars and pedestrians with diverse motion patterns. However, the standard IMM has limitations in combining motion models with different state definitions, so it cannot contain a complementary set of models that accurately work for all motion patterns. In this paper, we propose IMM-based adaptive target tracking with heterogeneous velocity representations and linear/curvilinear motion models. It can integrate four motion models with different state definitions and dimensions to be completely complimentary for all types of motions. We experimentally demonstrate the effectiveness of the proposed method with accuracy for various motion patterns using two types of datasets: synthetic datasets and real datasets. Experimental results show that the proposed method achieves the adaptive target tracking for diverse types of motion and also for various objects such as cars, pedestrians, and drones in the real world. [ABSTRACT FROM AUTHOR]

Summary: To avert catastrophic failure in the structures, joints are typically designed to yield, but not fail, so that energy accumulated under cyclic loading is dissipated. Eventually, this renders the structural joints to be characteristically weaker and more vulnerable than the members. Yet, damage detection research mostly assumes damage in the members only. This article proposes a model‐based predictor–corrector algorithm that uses an interacting filtering approach to efficiently estimate joint damage in the presence of input and measurement uncertainties. For the predictor model, a novel strain‐displacement relationship specific to semi‐rigid frames is developed to map nodal displacements to corresponding strain measurements. The proposed estimation method embeds robustness against non‐stationary input (e.g., seismic excitation) in the state filter, itself. For this, an output injection technique is integrated within the state filter. The modified state filter (robust Kalman filter) runs within an enveloping parameter filter (particle filter) to simultaneously estimate the system states and joint damage parameters, respectively, using the response signal. Strain has been adopted as measurement since it is frame independent (beneficial for seismic activity) and also comparatively cheaper to use. Numerical studies are performed on a two‐dimensional (2‐D) three‐story three‐bay shear frame for different joint damage locations and severities. The sensitivity and the stability of the proposed approach are further investigated. Experimental validation of the proposed algorithm is carried out on a 2‐D steel frame. [ABSTRACT FROM AUTHOR]

This paper proposes a class of algorithms for analyzing event count time series, based on state space modeling and Kalman filtering. While the dynamics of the state space model is kept Gaussian and linear, a nonlinear observation function is chosen. In order to estimate the states, an iterated extended Kalman filter is employed. Positive definiteness of covariance matrices is preserved by a square-root filtering approach, based on singular value decomposition. Non-negativity of the count data is ensured, either by an exponential observation function, or by a newly introduced “affinely distorted hyperbolic” observation function. The resulting algorithm is applied to time series of the daily number of seizures of drug-resistant epilepsy patients. This number may depend on dosages of simultaneously administered anti-epileptic drugs, their superposition effects, delay effects, and unknown factors, making the objective analysis of seizure counts time series arduous. For the purpose of validation, a simulation study is performed. The results of the time series analysis by state space modeling, using the dosages of the anti-epileptic drugs as external control inputs, provide a decision on the effect of the drugs in a particular patient, with respect to reducing or increasing the number of seizures.

Video sequences are frequently contaminated by noise throughout the acquisition process, resulting in considerable degradation of video display quality. In this paper, we present a novel method of video filtering. The proposed filter is developed from an optimization problem in which a Bayesian term and a noisy video sequence prior distribution are combined. The method begins by segmenting the video sequence into space-time blocks and then substituting each noisy block by a weighted average of non-local neighbor blocks. Gradient-based weights are used to dynamically adjust the edge preservation and smoothness of the reference block. The obtained formulation enables nonlinear filtering and, hence, preserving key features such as edges and corners while using the intrinsic Bayesian filtering framework. Experiments on different video sequences with varying degrees of noise show that the proposed method performs better than state-of-the-art video filtering approaches.

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%. [ABSTRACT FROM AUTHOR]

We present a new model for reliability analysis that is able to employ condition monitoring data in order to simultaneously monitor the latent degradation level and track failure progress over time. The method presented in this paper is a bridge between Bayesian filtering and classical binary classification, both of which have been employed successfully in various application domains. The Kalman filter is used to model a discrete-time continuous-state degradation process that is hidden and for which only indirect information is available through a multi-dimensional observation process. Logistic regression is then used to connect the latent degradation state with the failure process that is itself a discrete-space stochastic process. We present a closed-form solution for the marginal log-likelihood function and provide formulas for few important reliability measures. A dynamic cost-effective maintenance policy is finally introduced that can employ sensor signals for real-time decision-making. We finally demonstrate the accuracy and usefulness of our framework via numerical experiments. [ABSTRACT FROM PUBLISHER]

The use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The algorithm is able to adapt to changes in the signal-to-noise ratio (SNR) over time, improving performance through windowed analysis and Bayesian criterion-based smoothing. By evaluating the algorithm on heart-rate (HR) data collected from Garmin Vivoactive 4 smartwatches worn by individuals with amyotrophic lateral sclerosis and multiple sclerosis, it is demonstrated that BF provides superior SNR tracking and smoothing compared with non-adaptive methods. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings.

As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models.

In stochastic estimation problems, we aim to estimate an unknown state of interest given a set of measurements received from noisy sensory devices, such as radar, light detection and ranging, etc. A common model of the measurements’ random error is white Gaussian noise. This noise model is usually used to derive an estimator of the unknown state; accordingly, the state is described as a random variable with a Gaussian density. In some applications, the unknown state is known to take only positive values, e.g., estimating size or dimension. In such a case, the classical approaches based on Gaussian densities might fail, i.e., produce a negative value. In this article, a recursive Bayesian filter is proposed based on modeling the unknown state as Wishart distributed random matrix. This model ensures that the probability densities of the random variables are restricted to positive real values, even though the measurement’s noise is still modeled as white Gaussian noise. The feasibility of the proposed Bayesian–Wishart filter is demonstrated within the framework of extended target tracking. A target contour measurement model of a symmetric star-convex shape is presented and integrated in the proposed filter to track the target’s extent. [ABSTRACT FROM AUTHOR]

This article presents a Bayesian approach for sequential detection of anomalies in the motion of a target and joint tracking. The anomaly is modeled as a binary (on/off) switching unknown control input that goes into action (begins to exist, or "switches on") thereby modifying the object dynamics; and by ceasing its activity (becoming nonexistent, or "switching off") returns the dynamics to nominal. The developed Bayesian framework brings together random finite set (RFS) theory to represent the switching nature of the anomaly, and optimal joint input and state estimation to sequentially update a hybrid state that incorporates a random vector for the kinematic state and a Bernoulli RFS for the unknown control input. In addition, a closed-form solution, the Gaussian-mixture hybrid Bernoulli filter (GM-HBF), has been developed to provide a customized solution for dynamic anomaly detection in the maritime domain characterized by linear Gaussian target dynamics. Based on the Ornstein–Uhlenbeck dynamic model for vessels, where the evolution of the object velocity is governed by a piecewise mean-reverting stochastic process, the anomaly can be represented by a change in the long-run mean velocity (i.e., the unknown control input) from the nominal condition that forces the vessel to deviate from its standard route. We demonstrate the effectiveness of the proposed GM-HBF in both simulated and real-world maritime surveillance applications and test its performance in the face of false measurements, detection uncertainty, and sensor data gaps. [ABSTRACT FROM AUTHOR]

Robust, accurate localization is crucial to many of the current challenges in robotics. In recent years, visual simultaneous localization and mapping (SLAM) approaches have been studied intensively. The majority of approaches realize an excellent performance through precise visual odometry (VO) and loop closing based on a graph optimization technique. However, as the graph size increases, the computation time required for the graph optimization increases significantly. This makes it difficult to implement on mobile devices and causes a decrease in online performance. We propose a novel Bayesian graph update (BGU) to accelerate the loop closing of graph-based SLAM. The proposed method comprises sequentially updating the graph each time a new edge is obtained instead of optimizing the entire graph. Consequently, the update latency of one loop closing is reduced. The fundamental idea is to update whole edges on the graph using a Bayesian filtering method. We propose a fast algorithm based on a decoupled extended Kalman filter; its calculation time is proportionate to the number of edges at most. We confirm the speed and accuracies of our BGU using a simulated dataset. We also demonstrate that our BGU works well with actual visual SLAM methods by equipping it on ORB-SLAM2. [ABSTRACT FROM AUTHOR]

Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO. [ABSTRACT FROM AUTHOR]

The widely used Bayesian filtering has a solid theoretical foundation and an efficient computational architecture, but it suffers from first-order Markovianity and the inability to learn from offline data. While recent research has utilized deep learning to overcome these limitations, issues remain with respect to theoretical rigor and scalability, resulting in the non-guaranteed optimality of filtering. In this work, we approach these problems from a biomimetic perspective. Firstly, a memory-based state evolution model is constructed inspired by the human cognitive mechanism, which enables recursive filtering while incorporating long-term state information. Secondly, a memory-biomimetic deep Bayesian filtering algorithm with good interpretability is designed, which improves the filtering process at multiple levels by naturally integrating recurrent neural networks and Bayesian filtering architecture. Thirdly, the Gaussian approximation implementation of the proposed filtering method is derived, which brings an efficient computational process with good analytical properties. Extensive experiments for airborne target tracking are performed to demonstrate the excellent estimation performance of the proposed method. • The serial correlated state evolution model is transformed into a memory-based model. • A generic joint state-memory Bayesian filtering framework is derived. • A point estimation implementation of the proposed filtering framework is derived. [ABSTRACT FROM AUTHOR]

Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot be parallelized. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about $5.62~\mu \text{s}$ for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications.

Deep learning-based methods monopolize the latest research in the field of thermal infrared (TIR) object tracking. However, relying solely on deep learning models to obtain better tracking results requires carefully selecting feature information that is beneficial to representing the target object and designing a reasonable template update strategy, which undoubtedly increases the difficulty of model design. Thus, recent TIR tracking methods face many challenges in complex scenarios. This paper introduces a novel Deep Bayesian Filtering (DBF) method to enhance TIR tracking in these challenging situations. DBF is distinctive in its dual-model structure: the system and observation models. The system model leverages motion data to estimate the potential positions of the target object based on two-dimensional Brownian motion, thus generating a prior probability. Following this, the observation model comes into play upon capturing the TIR image. It serves as a classifier and employs infrared information to ascertain the likelihood of these estimated positions, creating a likelihood probability. According to the guidance of the two models, the position of the target object can be determined, and the template can be dynamically updated. Experimental analysis across several benchmark datasets reveals that DBF achieves competitive performance, surpassing most existing TIR tracking methods in complex scenarios. [ABSTRACT FROM AUTHOR]