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This study focuses on the improvement of the tracking performance of space observation radar systems. Coherent radar systems usually provide an observation vector including four observables (range, range rate, azimuth, elevation) measured at a given epoch. Moreover, for some coherent radars, such as the TIRA system, it is possible to determine an additional parameter, the range rate, which is extracted from the collected complex amplitude. The tracking performance can be improved by increasing the observation parameter accuracy. The influence of each parameter on the overall tracking performance can be assessed by conducting a sensitivity analysis. The investigation shows that the best way to increase the accuracy of the state vector is by improving the angle estimates. The TIRA system is equipped with a tracking radar and an imaging radar. A powerful solution to improve the tracking performance is the realization of a multi-sensor data fusion between the two radars of the system. [ABSTRACT FROM AUTHOR]

Electrically excited synchronous motors have the advantages of wide speed range, high overload capacity, and adjustable power factor, and are widely used in high-power transmission fields, where position sensorless control technology is an important part of the speed regulation of electrically excited synchronous motors. A back-to-back voltage-source converter structure, current frequency control, sliding mode observer, and Kalman filter are presented, followed by two position sensorless control schemes using only current frequency control and hybrid control combining current frequency control and sliding mode observer. Simulation and experimental results verify that both control schemes can achieve variable frequency starting and speed regulation of electrically excited synchronous motors without position sensors, but the hybrid control scheme combining current frequency control and sliding mode observer has the advantages of high-power factor and high practicality, and is more suitable for high-power drive applications. [ABSTRACT FROM AUTHOR]

The threat of debris collisions in Low Earth Orbit (LEO) has driven many researchers to develop Active Debris Removal (ADR) missions to manage large debris in LEO. A modern method of capturing debris with a non-rigid tether has been a recent research focus because of difficult post capture dynamics associated with controlling an uncooperative debris through a tether. To facilitate these control schemes, this paper proposes a new two-stage inertia estimator that estimates all principal and products of inertia for an uncooperative space debris using LiDAR and tether force measurements made on-board a CubeSat sized chaser satellite. The method proposed in this work estimates the debris center of mass with a novel pseudo measurement Kalman Filter so that the full inertia tensor can be subsequently estimated using an additional iterative parameter identification algorithm. In addition, the paper achieves online inertia estimation without assuming the tether connection point on the debris by approximating its location using tether tension measurements. The proposed control and estimation scheme is shown in this paper to estimate the full debris inertia tensor even with frequent tether slackness. Two simulation scenarios are presented in this paper, one where the connection point of the tether on the debris is approximated using noiseless measurements of tether tension, and a second where the location of the tether connection point is approximated using noisy measurements of tether tension. For the simulation conditions used in this research, it is shown that the proposed two-stage estimator (TSE) is successful for both cases that the debris is inertially asymmetrical and tri-inertial with non-zero products of inertia. • Determinable input torque without assumption of Body Frame tether connection points. • Novel Two-Stage Estimator for all principle and products of inertia for space debris. • Responsive Kalman Filter formulations under frequent tether slackness. • Center of mass estimation and tracking for debris object using LIDAR sensor model and Kalman Filter. • Station keeping control law for CubeSat chaser satellite. [ABSTRACT FROM AUTHOR]

In this paper, new path optimization algorithms are developed for uncrewed aerial vehicle (UAV) self-localization and target tracking, exploiting beacon (landmark) bearings and angle-of-arrival (AOA) measurements from a manoeuvring target. To account for time-varying rotations in the local UAV coordinates with respect to the global Cartesian coordinate system, the unknown orientation angle of the UAV is also estimated jointly with its location from the beacon bearings. This is critically important, as orientation errors can significantly degrade the self-localization performance. The joint self-localization and target tracking problem is formulated as a Kalman filtering problem with an augmented state vector that includes all the unknown parameters and a measurement vector of beacon bearings and target AOA measurements. This formulation encompasses applications where Global Navigation Satellite System (GNSS)-based self-localization is not available or reliable, and only beacons or landmarks can be utilized for UAV self-localization. An optimal UAV path is determined from the optimization of the Bayesian Fisher information matrix by means of A- and D-optimality criteria. The performance of this approach at different measurement noise levels is investigated. A modified closed-form projection algorithm based on a previous work is also proposed to achieve optimal UAV paths. The performance of the developed UAV path optimization algorithms is demonstrated with extensive simulation examples. [ABSTRACT FROM AUTHOR]

Robotic force control is crucial for precise polishing and has a significant influence on the final effects. The blade has a free-form surface in space, and the curvature changes drastically, making traditional impedance control feedback untimely. To solve this problem, this paper proposes an adaptive impedance control method for blade polishing based on Kalman filter. The force data is denoised by Kalman filtering to obtain the real force data, then the data is gravity compensated to obtain the real polishing force. The method analyzes the influences of stiffness change and displacement change on the polishing force, and establishes a stiffness and displacement coupling compensation model. The method achieves timely feedback when the robot copes with unknown environmental stiffness changes. In addition, the Lyapunov function is applied to verify the stability of the method during implementation. Four processing conditions are simulated by using Matlab Simulink. The results indicate that the proposed method can provide faster response and higher force tracking accuracy by adjusting the reference position when the environment changes. In the experiment of polishing blade, the roughness is reduced to below Ra0.32 μm and fluctuation range of polishing force is within ±1 N. The force control method performance is significantly improved and the blade surface quality is effectively improved. [ABSTRACT FROM AUTHOR]

The growing integration of distributed energy resources underscores the critical importance of having precise insights into the dynamics of an electrical power system (EPS). Consequently, an estimator must align with the EPS dynamics to enhance the overall reliability, safety, and system stability. This alignment ensures that operators can make informed decisions during system operations. An initial step in gaining insight into the system's state involves examining its state vector, which is represented by voltage phasors. These results are derived through the application of a distributed state-estimation process in large-scale systems. This study delved into the effectiveness of Bayesian filters, with a particular emphasis on the extended Kalman filter (EKF) algorithm in the context of distributed state estimation. To analyze the outcomes, the nodal partitioning process was incorporated within the distributed state-estimation framework. The synergy between the EKF algorithm and the partitioning method was evaluated using the IEEE118 test system. [ABSTRACT FROM AUTHOR]

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection. [ABSTRACT FROM AUTHOR]

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis. [ABSTRACT FROM AUTHOR]

With the development of science and technology, people have higher and higher requirements for robots. The application of robots in industrial production is also increasing, and there are more applications in people’s lives. Therefore, robots must have a better ability to receive and process the external environment. Therefore, visual servo system appears. Pose estimation is a major problem in the current vision system. It has great application value in positioning and navigation, target tracking and recognition, virtual reality and motion estimation. Therefore, this paper put forward the research of robot arm pose estimation and control based on machine vision. This paper first analyzed the technology of machine vision, and then carried out experiments. The accuracy and stability of the two methods for robot arm pose estimation were compared. The experimental results showed that when the noise of Kalman’s centralized data fusion method was 1 pixel, the maximum error of the X-axis angle was only 0.55, and the average error was 0.02. In Kalman’s distributed data fusion method, the average error of X-axis displacement was 0.06, and the maximum value was 17.66. In terms of accuracy, Kalman’s centralized data fusion method was better. In terms of stability, Kalman’s centralized data fusion method was also better. However, in general, these two methods had very good results, and could accurately control the position and posture of the manipulator. [ABSTRACT FROM AUTHOR]

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy. [ABSTRACT FROM AUTHOR]

Online multi-object tracking (MOT) techniques are instrumental in monitoring workers' positions and identities in construction settings. Traditional approaches, which employ deep neural networks (DNNs) for detection followed by body similarity matching, often overlook the significance of clear head features and stable head motions. This study presents a novel bidirectional tracking method that integrates intra-frame processing, which combines head and body analysis to minimize false positives and inter-frame matching to control ID assignment. By leveraging head information for enhanced body tracking, the method generates smoother trajectories with reduced ID errors. The proposed method achieved a state-of-the-art (SOTA) performance, with a multiple-object tracking accuracy (MOTA) of 95.191%, higher-order tracking accuracy (HOTA) of 78.884% and an identity switch (IDSW) count of 0, making it a strong baseline for future research. [ABSTRACT FROM AUTHOR]

Many complicated dynamical events may be broken down into simpler pieces and efficiently described by a system that shifts among a variety of conditionally dynamical modes. Building on switching linear dynamical systems, we develop a new model that extends the switching linear dynamical systems for better discovering these dynamical modes. In the proposed model, the linear dynamics of latent variables can be described by a higher-order vector autoregressive process, which makes it feasible to evaluate the higher-order dependency relationships in the dynamics. In addition, the transition of switching states is determined by a stick-breaking logistic regression, overcoming the limitation of a restricted geometric state duration and recovering the symmetric dependency between the switching states and the latent variables from asymmetric relationships. Furthermore, logistic regression evidence potentials can appear as conditionally Gaussian potentials by utilizing the Pólya-gamma augmentation strategy. Filtering and smoothing algorithms and Bayesian inference for parameter learning in the proposed model are presented. The utility and versatility of the proposed model are demonstrated on synthetic data and public functional magnetic resonance imaging data. Our model improves the current methods for learning the switching linear dynamical modes, which will facilitate the identification and assessment of the dynamics of complex systems. [ABSTRACT FROM AUTHOR]

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method (CM), the proposed tracking method (TM), and the variational method (VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost. [ABSTRACT FROM AUTHOR]

In order to improve the anti-interference ability and control accuracy of wind power brake, the improved active disturbance rejection control and Kalman filter algorithm are proposed based on the traditional three-closed-loop system. The traditional "large bandwidth, small error" is improved to "small bandwidth, small error" by introducing the speed tracking condition in the extended state observer to improve the observation efficiency of the observer. A new exponential reaching law sliding mode control is introduced into the error feedback control rate to enhance the control effect. The Kalman filter algorithm is introduced into the current loop to reduce the current noise. The mathematical model of the electric braking system is established by MATLAB/Simulink, and compared with the traditional active disturbance rejection control strategy and PID control strategy. The results showed that the improved control strategy can improve the response speed and reduce the starting torque and current noise. The brake showed good control effect and anti-interference ability under different operating conditions, which enhanced the system's stability and dynamic performance. [ABSTRACT FROM AUTHOR]

Object detection and tracking are pivotal tasks in machine learning, particularly within the domain of computer vision technologies. Despite significant advancements in object detection frameworks, challenges persist in real-world tracking scenarios, including object interactions, occlusions, and background interference. Many algorithms have been proposed to carry out such tasks; however, most struggle to perform well in the face of disturbances and uncertain environments. This research proposes a novel approach by integrating the You Only Look Once (YOLO) architecture for object detection with a robust filter for target tracking, addressing issues of disturbances and uncertainties. The YOLO architecture, known for its real-time object detection capabilities, is employed for initial object detection and centroid location. In combination with the detection framework, the sliding innovation filter, a novel robust filter, is implemented and postulated to improve tracking reliability in the face of disturbances. Specifically, the sliding innovation filter is implemented to enhance tracking performance by estimating the optimal centroid location in each frame and updating the object's trajectory. Target tracking traditionally relies on estimation theory techniques like the Kalman filter, and the sliding innovation filter is introduced as a robust alternative particularly suitable for scenarios where a priori information about system dynamics and noise is limited. Experimental simulations in a surveillance scenario demonstrate that the sliding innovation filter-based tracking approach outperforms existing Kalman-based methods, especially in the presence of disturbances. In all, this research contributes a practical and effective approach to object detection and tracking, addressing challenges in real-world, dynamic environments. The comparative analysis with traditional filters provides practical insights, laying the groundwork for future work aimed at advancing multi-object detection and tracking capabilities in diverse applications. [ABSTRACT FROM AUTHOR]

Interest towards electric vehicle adoption is on the rise due to the lower running and maintenance cost it offers, along with zero tailpipe emissions. Range anxiety is one of the only concern that affects the adoption of electric vehicles. The state of charge of the traction battery pack has to be accurately determined and provided to the user to avoid range anxiety. Minute battery parameters has to be considered to improve the accuracy of the state of charge determination. In order to overcome the problem of range anxiety, an innovative strategy that takes into account how vibrations affect the performance of EV batteries is developed in this research. By doing this, the state of charge estimation precision is improved and thereby raises the driver's faith in electric vehicles. The impacts and vibrations felt on the traction battery pack during driving would lead to heat generation. The heat generated is found to be highest when the vibrations resonate at the natural frequencies of the traction battery pack. The natural frequency of the battery pack is considered when the battery is kept in the battery chamber of the two-wheeler electric vehicle. The vibrations at natural frequency produces heat which is accounted for when the state of charge is determined. To obtain accurate state of charge estimation, a Kalman filter-based approach is used. The Kalman filter-based estimation uses the conventional methods which are the open circuit voltage method and the Coulomb counting method to improve the estimation process along with the consideration of the heat component due to vibrations and impact. The vibration analysis is performed using MATLAB, while the state of charge determination is implemented in hardware and the Kalman estimation done using Python. The system is modelled on an electric two-wheeler platform and the testing is done to compare the state of charge accuracy of the open circuit voltage method, the Coulomb counting method and the Kalman filter-based estimation approach. The inclusion of the vibrational heat analysis for State of Charge estimation in the hardware testing of the electric two-wheeler provides an accurate state of charge value. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a distributed joint parameter and state variables estimation algorithm for large‐scale state‐space interconnected systems. In this distributed estimation scheme, each interconnected sub‐system is described by a linear discrete‐time state space mathematical model. Each sub‐system is supposed to be controlled by an intelligent controller that can communicate with its interconnected neighbors and exchange information, such as state variables. The proposed approach comprises two recursive estimation algorithms, a parameter estimation algorithm considering the state space model and a distributed Kalman filter for state variables estimation. It is a fully distributed cooperative approach that allows to reduce complexity and saves computational and communication resources. Theoretical analysis and numerical examples are provided to prove the feasibility and effectiveness of this joint estimation algorithm. [ABSTRACT FROM AUTHOR]

The emergence of Internet of things (IoT) technology has significantly influenced daily life and promoted the development of intelligent transportation systems and pilotless automobiles. In complex traffic environments such as curves and intersections, pilotless automobiles face some problems in pedestrian recognition, such as low detection distance accuracy and occlusion. To improve the timeliness of end-to-end information interaction and the early warning effect of anti-collision systems, a pedestrian target information fusion algorithm based on adaptive machine learning is proposed in the environment of Internet of vehicles (IoV). The pedestrian information acquired by roadside and on-board cameras is pretreated using a Kalman filter; then, the best position of the pedestrian target can be estimated and confirmed by integrating the time–space alignment, fuzzy association, and Kalman filtering results. Experiments show that both the maximum absolute error and the absolute mean error of the pedestrian trajectory are reduced significantly, i.e., by 45.0% and 58.5%, respectively, compared with those before trajectory fusion. Compared with S-LSTM and S-GAN methods, reasoning time is greatly reduced.Thus, the accuracy of pedestrian trajectory detection is improved by the fusion algorithm, and the warning accuracy of the system is enhanced. [ABSTRACT FROM AUTHOR]

Tracking trajectories of the unconnected vehicles contributes to the improvement of traffic efficiency and safety. However, the effects of occlusions on the accuracy and reliability of the tracking results are nonnegligible. To address this issue, a modified multiple objects tracking algorithm was proposed to reduce the loss of trajectories caused by occlusions. The proposed algorithm was based on the multiple objects detection results, in which the motion states of the detected objects were determined. Further, the Kalman filter was employed to predict the trajectories, and each trajectory was uniquely matched to the detected object labeled with a tracking identity (ID) using the Hungarian algorithm. Afterward, a flexible life cycle strategy was proposed in terms of the speeds and accelerations of the detected objects, which controls the life cycle of the labeled trajectories when the occlusion occurred and guarantees the continuity of trajectories. The proposed tracking algorithm was tested on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data set, and the classification of events, activities, and relationships workshops, multiple object tracking (CLEAR-MOT) metrics was introduced to evaluate the performance of the proposed algorithm. The results indicated that the proposed algorithm with appropriate life cycle apparently increased the precision and accuracy and reduced the influence of occlusions on multiple objects tracking. Future work will concentrate on field implementations of the algorithm, and various scenarios and weather conditions will be taken into account. [ABSTRACT FROM AUTHOR]

This paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input–output datasets (using voltage–current sensors), with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the fifth-order nonlinear model of the induction motor, the rotor speed is estimated with the optimized EKF algorithm, and the algorithm is tested experimentally. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) is widely used for its high accuracy, wide coverage, and strong real-time performance. However, limited by the navigation signal mechanism, satellite signals in urban canyons, bridges, tunnels, and other environments are seriously affected by non-line-of-sight and multipath effects, which greatly reduce positioning accuracy and positioning continuity. In order to meet the positioning requirements of human and vehicle navigation in complex environments, it was necessary to carry out this research on the integration of multiple signal sources. The Fifth Generation (5G) signal possesses key attributes, such as low latency, high bandwidth, and substantial capacity. Simultaneously, 5G Base Stations (BSs), serving as a fundamental mobile communication infrastructure, extend their coverage into areas traditionally challenging for GNSS technology, including indoor environments, tunnels, and urban canyons. Based on the actual needs, this paper proposes a system algorithm based on 5G and GNSS joint positioning, aiming at the situation that the User Equipment (UE) only establishes the connection with the 5G base station with the strongest signal. Considering the inherent nonlinear problem of user position and angle measurements in 5G observation, an angle cosine solution is proposed. Furthermore, enhancements to the Sage–Husa Adaptive Kalman Filter (SHAKF) algorithm are introduced to tackle issues related to observation weight distribution and adaptive updates of observation noise in multi-system joint positioning, particularly when there is a lack of prior information. This paper also introduces dual gross error detection adaptive correction of the forgetting factor based on innovation in the iterative Kalman filter to enhance accuracy and robustness. Finally, a series of simulation experiments and semi-physical experiments were conducted. The numerical results show that compared with the traditional method, the angle cosine method reduces the average number of iterations from 9.17 to 3 with higher accuracy, which greatly improves the efficiency of the algorithm. Meanwhile, compared with the standard Extended Kalman Filter (EKF), the proposed algorithm improved 48.66 % , 35.17 % , and 38.23 % at 1 σ / 2 σ / 3 σ , respectively. [ABSTRACT FROM AUTHOR]

This paper presents a sensor fusion method for navigation of unmanned underwater vehicles. The method combines Lie theory into Kalman filter to estimate and compensate for the misalignment between the sensors: inertial navigation system and Doppler Velocity Log (DVL). In the process and measurement model equations, a 3-dimensional Euclidean group ( SE (3) ) and 3-sphere space ( S 3 ) are used to express the pose (position and attitude) and misalignment, respectively. SE (3) contains position and attitude transformation matrices, and S 3 comprises unit quaternions. The increments in pose and misalignment are represented in the Lie algebra, which is a linear space. The use of Lie algebra facilitates the application of an extended Kalman filter (EKF). The previous EKF approach without Lie theory is based on the assumption that a non-differentiable space can be approximated as a differentiable space when the increments are sufficiently small. On the contrary, the proposed Lie theory approach enables exact differentiation in a differentiable space, thus enhances the accuracy of the navigation. Furthermore, the convergence and stability of the internal parameters, such as the Kalman gain and measurement innovation, are improved. [ABSTRACT FROM AUTHOR]

The lack of discernible vehicle contour features in low-light conditions poses a formidable challenge for nighttime vehicle detection under hardware cost constraints. Addressing this issue, an enhanced histogram of oriented gradients (HOGs) approach is introduced to extract relevant vehicle features. Initially, vehicle lights are extracted using a combination of background illumination removal and a saliency model. Subsequently, these lights are integrated with a template-based approach to delineate regions containing potential vehicles. In the next step, the fusion of superpixel and HOG (S-HOG) features within these regions is performed, and the support vector machine (SVM) is employed for classification. A non-maximum suppression (NMS) method is applied to eliminate overlapping areas, incorporating the fusion of vertical histograms of symmetrical features of oriented gradients (V-HOGs). Finally, the Kalman filter is utilized for tracking candidate vehicles over time. Experimental results demonstrate a significant improvement in the accuracy of vehicle recognition in nighttime scenarios with the proposed method. [ABSTRACT FROM AUTHOR]

The performance of low-cost smart terminals is limited by the performance of their low-cost Global Navigation Satellite System (GNSS) hardware and chips, as well as by the impact of complex urban environments, which affect the positioning accuracy and stability of GNSS services. To this end, this paper proposes a robust adaptive Kalman filter for different environments that can be applied after data preprocessing. Based on the Kalman filter algorithm, a robust estimation approach is introduced into real-time kinematic (RTK) positioning to make judgments on the abnormal observation values of low-cost smart terminals, which amplifies the variance and covariance of the outlier observation equation, and reduces the impact of outliers on positioning performance. The Institute of Geodesy and Geophysics III (IGG III) function is used for regulation purposes, where prior information is modified and refreshed using the equivalent weight matrix and adaptive factors, thus reducing the impact of system model errors on system state estimation results. In addition, a robust factor is defined to adjust positioning deviation weighting between the pre- and post-test robust estimates. The experimental results show that after robust RTK positioning in the static experiments, the overall improvement in positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and low-cost M8 receiver reached 29.6%, 31.3%, 32.1%, and 30.7%, respectively. Similarly, after applying the proposed robust method in the dynamic experiments, the overall positioning accuracies of the Xiaomi 8, Huawei P40, Huawei mate40, and the low-cost M8 receiver improved by 28.3%, 32.9%, 35.4%, and 26.2%, respectively. The experimental results reveal that an excellent positioning effect of a smartphone is positively correlated with robust RTK positioning performance. However, it is worth noting that when the positioning accuracy reaches a high level, such as the positioning results achieved using low-cost receivers, the robustness performance shows a relatively decreasing trend. This finding suggests that under the condition of high positioning accuracy, the sensitivity of specific positioning equipment to interference sources may increase, resulting in a decline in the effect of robust RTK positioning. [ABSTRACT FROM AUTHOR]

The objective of this paper is to tackle the issue of the degraded navigation accuracy of the inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system in urban applications, especially under complex environments. This study utilizes historical state estimates and proposes a multi-step pseudo-measurement adaptive Kalman filter (MPKF) algorithm based on the filter performance evaluation. First, taking advantage of the independence between INS and GNSS, the enhanced second-order mutual difference (SOMD) algorithm is utilized for estimating the noise variance of the GNSS, which is decoupled from the estimate error of state and used as a module for filter performance evaluation. Then, the construction of the proposed method is presented, together with the analysis of the noise variance of multi-step pseudo-measurement. Ultimately, the efficacy of the MPKF is confirmed through a real-world vehicle experiment involving a tightly-coupled INS/GNSS integrated navigation application, demonstrating a noteworthy enhancement in navigation precision within densely wooded and built-up areas. Compared to the standard EKF and enhanced redundant measurement-based adaptive Kalman filter (ERMAKF), the proposed algorithm improves the positioning accuracy by 48% and 34%, velocity accuracy by 50% and 35%, and attitude accuracy by 38% and 48%, respectively, in the urban building segment. [ABSTRACT FROM AUTHOR]

This paper presents a technique for tracking the high-speed motion of a multilink system using inertial measurement units (IMUs) in a new sensor arrangement, an approach which is referred to as dynamic measurements fusion. The proposed technique incorporates accelerometers with traditional gyroscopes to measure joint angular velocities, while joint angles are measured with magnetometers. Comparative studies with conventional techniques show that the proposed technique tracks the motion of a multilink system accurately at both low (0.5 m/s) and high (5 m/s) speeds. Further analysis with different levels of measurement noise demonstrates the robustness of the proposed technique and its overall capability for tracking joint angular velocities and angles. [ABSTRACT FROM AUTHOR]

Automatic fire detection is an interesting challenge for several researches particularly in video surveillance application. Nowadays, it is required to exactly locate and recognize fire regions to avoid damage as soon as possible and to overcome the available fire detection methods limitations. Indeed, it is proposed in this work to combine the advanced computer vision techniques with the deep learning models. The presented approach aims to detect and track fire regions in video sequences. A fire detector is firstly developed by tuning the tiny-You Only Look Once version 3 (YOLOv3) network hyperparameters. The detected fire regions identified by their bounding boxes positions are then fed into an accurate fire tracker to avoid the missed detections caused by the detector. It is based on exploiting the Kalman filter to relocate the fire area with a more accurate estimation of its position. The overall performance of the novel approach, evaluated on the created dataset, is clearly enhanced from 94.70 to 96.70% in terms of true positives rate. This is thanks to the association of the fire tracker with the detector. This effective combination is demonstrated in experimental results where the number of the recognized fire objects is increased with a reduction of the false positives to only 0.0155 compared to the fire objects number detected without the tracker. [ABSTRACT FROM AUTHOR]

Geodetic time series are usually modeled with a deterministic approach that includes trend, annual, and semiannual periodic components having constant amplitude and phase-lag. Although simple, this approach neglects the time-variability or stochasticity of trend and seasonal components, and can potentially lead to inadequate interpretations, such as an overestimation of global navigation satellite system (GNSS) station velocity uncertainties, up to masking important geophysical phenomena. In this contribution, we generalize previous methods for determining trends and seasonal components and address the challenge of their time-variability by proposing a novel linear additive model, according to which (i) the trend is allowed to evolve over time, (ii) the seasonality is represented by a fractional sinusoidal waveform process (fSWp), accounting for possible non-stationary cyclical long-memory, and (iii) an additional serially correlated noise captures the short term variability. The model has a state space representation, opening the way for the evaluation of the likelihood and signal extraction with the support of the Kalman filter (KF) and the associated smoothing algorithm. Suitable enhancements of the basic methodology enable handling data gaps, outliers, and offsets. We demonstrate the advantage of our method with respect to the benchmark deterministic approach using both observed and simulated time series and provide a fair comparison with the Hector software. To that end, various geodetic time series are considered which illustrate the ability to capture the time-varying stochastic seasonal signals with the fSWp. [ABSTRACT FROM AUTHOR]

Emergency Vehicle Preemption (EVP) system plays an important role in reducing the response time of emergency vehicles by around 15–50%. Emergency Vehicle location estimation during Global Positioning System (GPS) outages is a challenging task in EVP. To address this issue, a location estimation scheme has been proposed by applying deep output kernel learning and  extended Kalman filter using Inertial Measurement Unit (IMU) dead reckoning. The proposed scheme reduces the error in the estimation of velocity, attitude, and position by dynamically adapting the noise parameters of the extended Kalman filter. This system provides navigation solutions for emergency vehicles. Initially, four test routes were selected and the Position, velocity and attitude (pitch and roll angle) were measured and applied to the proposed scheme. Training and testing were conducted for the measured datasets. The performance of the proposed scheme was measured using five statistical methods during training and testing, and a comparison was made with other existing methods. The simulation results show that the proposed scheme performed well, in the four test routes. Finally, two different case studies were conducted using the proposed scheme and the performance was compared with three other methods. According to the results, the proposed scheme showed improvements in detection accuracy compared to the existing methods during GPS outages of 71.94% and 62.83% for Trajectory-1 and Trajectory-2, respectively. [ABSTRACT FROM AUTHOR]

The influence of the standard deviation of the motion characteristic equation and the measurement frequency on the measurement accuracy of the track irregularity is studied in depth. The model is established by combining the GNSS / INS and NHC algorithms, and the covariance method is used for quantitative analysis. Through simulation experiments, it is verified that the addition of motion characteristic equation improves the measurement accuracy of track irregularity. When the standard deviation of the motion characteristic equation is constant, the accuracy increases with the increase of frequency. When the frequency is constant, the smaller the standard deviation of the motion characteristic equation, the higher the orbit measurement accuracy. Given the appropriate standard deviation of the motion characteristic equation can meet the requirements of track irregularity measurement accuracy. The field experimental results are basically consistent with the simulation results, which proves the correctness of the proposed method. The research conclusions provide a reference for improving the measurement accuracy of track irregularity. [ABSTRACT FROM AUTHOR]

Navigation challenges have been posed on the conventional systems in greenhouses, such as significant light variations, narrow crop row spacing, failure to receive satellite signals, and rigid travel paths. In this study, autonomous navigation was proposed to integrate laser vision with 3D SLAM (simultaneous localization and mapping). Environmental data was collected from 3D LIDAR VLP-16 (Velodyne LiDAR) and an IMU (inertial measurement unit). The LIO-SAM (tightly-coupled lidar inertial odometry via smoothing and mapping) was employed to generate 3D point cloud maps, which were subsequently downscaled to the raster maps. This integration included the data from wheeled odometers and visual odometers using an Extended Kalman filter. Visual odometers provided the positional information to correct and update the state prediction of the mobile platform, functioning as a local localization tool. Additionally, the adaptive Monte Carlo localization data introduced the weights to the ndt-matching (normal distributions transform matching), in order to enhance the accuracy of global localization. Moreover, the autonomous walking system was utilized as the A* algorithm and dynamic window algorithm for the path creation and autonomous navigation. The navigation system of autonomous walking was composed of a remote monitoring platform and an on-board system. Specifically, the remote monitoring platform was responsible for selecting the working mode of the onboard system, then releasing the instruction of target points, and finally displaying the location. The on-board system was the executor of the instructions, in order to receive and execute the task instructions ordered by the monitoring platform. The remote monitoring and onboard systems were combined to realize the autonomous navigation task of the greenhouse transportation robot, according to real-time communication through a wireless network. Experimental results showed that the maximum relative error, the maximum absolute error, and the root mean square error of the greenhouse navigation map constructed by the LIO-SAM algorithm reached 9.9%, 0.081 and 0.063 m, respectively. The improved localization algorithm reduced the horizontal and vertical deviations in the autonomous walking system (less than 0.020 and 0.090 m, respectively). The new system maintained mean values of horizontal deviation, longitudinal deviation, and heading declination below 0.120 m, 0.100 m, and 8.5°, respectively, with standard deviations of less than 0.070 m, 0.140 m, and 6.6°, respectively. The approach had significantly improved the accuracy of positioning and navigation. This navigation scheme can fulfill the need for high-precision localization and navigation in the autonomous walking systems within the greenhouse. The findings can provide theoretical and technical support to autonomous mobile platforms. [ABSTRACT FROM AUTHOR]

In order to assess military requirement scientifically and effectively, through steps division of stages, determination of assessment object and construction of assessment indicators are systematically described the military requirement assessment problem. Adopt formal language, focus on description of requirement value in the requirements demonstration stage, capability valve in the implementation stage and assessment satisfaction value in the inspection stage assessment. Study their assessments trends. Based on Kalman filter ideas, conduct experimental analysis on the proposed formal model. The results show that assessment trend can gradually approach the ideal value, and errors can converge. Factors such as assessment cycle, initial assessment value, expected error, measurement error will have an impact on both the assessment trend and the assessment results. Relationship between the comprehensive assessment results and the sub assessment indicators are non-linear. The results indicate that the basic laws of assessment can be characterized by established formal model. Research results can provide a theoretical basis of military requirement assessment. [ABSTRACT FROM AUTHOR]

Gravity on Earth is of great interest in geodesy, geophysics, and natural resource exploration. Ship-based gravimeters are a widely used instrument for the collection of surface gravity field data in marine regions. However, due to the considerable distance from the sea surface to the seafloor, the spatial resolution of surface gravity data collected from ships is often insufficient to image the detail of seafloor geological structures and to explore offshore natural minerals. Therefore, the development of a mobile underwater gravimetry system is necessary. The GraviMob gravimeter, developed for a moving underwater platform by Geo-Ocean (UMR 6538 CNRS-Ifremer-UBO-UBS), GeF (UR4630, Cnam) and MAPPEM Geophysics, has been tested over the last few years. In this study, we report on the high-resolution gravity measurements from the GraviMob system mounted on an Autonomous Underwater Vehicle, which can measure at depths of up to several kilometres. The dedicated GraviMob underwater gravity measurements were conducted in the Mediterranean Sea in March 2016, with a total of 26 underwater measurement profiles. All these measurement profiles were processed and validated. In a first step, the GraviMob gravity measurements were corrected for temperature based on a linear relationship between temperature and gravity differences. Through repeated profiles, we acquired GraviMob gravity measurements with an estimated error varying from 0.8 to 2.6 mGal with standard deviation after applying the proposed temperature correction. In a second step, the shipborne gravity data were downward continued to the measurement depth to validate the GraviMob measurements. Comparisons between the corrected GraviMob gravity anomalies and downward continued surface shipborne gravity data revealed a standard deviation varying from 0.8 to 3.2 mGal and a mean bias value varying from −0.6 to 0.6 mGal. These results highlight the great potential of the GraviMob system in measuring underwater gravity. [ABSTRACT FROM AUTHOR]

Point cloud densification is essential for understanding the 3D environment. It provides crucial structural and semantic information for downstream tasks such as 3D object detection and tracking. However, existing registration-based methods struggle with dynamic targets due to the incompleteness and deformation of point clouds. To address this challenge, we propose a Kalman-based scene flow estimation method for point cloud densification and 3D object detection in dynamic scenes. Our method effectively tackles the issue of localization errors in scene flow estimation and enhances the accuracy and precision of shape completion. Specifically, we introduce a Kalman filter to correct the dynamic target's position while estimating long sequence scene flow. This approach helps eliminate the cumulative localization error during the scene flow estimation process. Extended experiments on the KITTI 3D tracking dataset demonstrate that our method significantly improves the performance of LiDAR-only detectors, achieving superior results compared to the baselines. [ABSTRACT FROM AUTHOR]

The inconsistency in state-of-charge (SOC) for electric vehicle batteries will cause component damage and lifespan reduction of batteries. Meanwhile, the consistency in the state-of-health (SOH) also negatively influences the consensus of SOC. To ensure the consensuses of SOC and SOH simultaneously, this paper introduces an innovative distributed optimal Kalman consensus filter (KCF) approach to battery management systems. In addition, at the stage where sensors transmit information to each other, a new event-triggering mechanism (ETM) based on dynamic information is proposed to reduce communication overhead effectively. Theoretical analysis verifies the optimality of the algorithm. By numerical simulations, the proposed event-triggered distributed optimal KCF (ET-DOKCF) method can improve the performance of SOC-SOH estimation and save communication resources. [ABSTRACT FROM AUTHOR]

Summary: This research presents the design of a fault‐tolerant system based on a dual extended Kalman filter (DEKF) and a bank of two high‐gain observers to ensure the continuous operation of an internal combustion engine (ICE) despite total and partial faults in the mass airflow (MAF) sensor, temperature sensor, and pressure sensor. The DEKF aims to estimate the air mass flow rate (m˙th$$ {\dot{m}}_{th} $$) through the throttle valve. The DEKF uses the temperature and pressure measured in the intake manifold to accurately estimate the (m˙th$$ {\dot{m}}_{th} $$), a failure in any of both sensors will not allow the correct functioning of the DEKF. Therefore, a bank of two high‐gain observers is proposed to estimate the pressure and temperature from only one measure. Observer 1 estimates the temperature and pressure from the pressure, and Observer 2 uses the temperature for estimating pressure and temperature. Hence, if the temperature or pressure sensors fail, the estimated temperature or pressure will replace the ill‐measure signal. The results show the effectiveness of the proposed fault‐tolerant system in the case of one sensor or multiple sensor faults. [ABSTRACT FROM AUTHOR]

Indoor user positioning is a crucial problem in modern life. It has wide usage in health, security, smart homes, etc. Global positioning system (GPS) is used outdoors, and it does not work effectively in indoor areas since many things can degrade GPS positioning accuracy. All solutions for indoor areas aim to provide low-cost and high-accuracy positioning. In this study, a low-cost indoor positioning algorithm is developed. The fingerprint signal map of the building is measured with built-in digital sensors in smart devices. The measurements consist of Wi-Fi, Bluetooth Low Energy, and magnetic field signals called data fusion. During the positioning phase, the proposed model, called improved cosine similarity, uses the cosine similarity and information gain method. Digital magnetometers measure magnetic fields with different approaches. In the proposed method, Kalman filter is used to reduce noise magnetic field signals since this variety can give rise to mistaken positioning. To compare the effectiveness of the proposed method, it was compared to K-nearest neighbor, support vector machines, linear discriminant analysis, artificial neural networks, decision trees, N-nearest neighbor, and binned neighbor algorithm. Based on the experimental data, it was concluded that the proposed architecture achieved higher accuracy rates by reducing distortion. [ABSTRACT FROM AUTHOR]

Aimed at the problem of position estimation error and buffeting caused by sliding mode observer in permanent magnet synchronous motor (PMSM) servo system, an improved two-stage filter sliding mode observer was proposed. Firstly, according to the mathematical model of PMSM, the estimation algorithm of stator current and back electromotive force using traditional sliding mode observer was designed. Secondly, the shortcomings of traditional sliding mode observer in the application of PMSM servo system were analyzed. Then, a new saturation function was proposed to replace the traditional symbolic function, and the sliding mode observer control law was improved. Finally, a two-stage filter composed of variable cutoff frequency filter and Kalman filter was used to obtain more accurate position estimation information of PMSM rotor. The system with traditional observer and the system with improved observer were simulated with Matlab respectively. The simulation results show that compared with the PMSM servo system with traditional observer, the rotational speed error of the PMSM servo system with improved observer is smaller and the estimation results are more accurate. [ABSTRACT FROM AUTHOR]

Due to the limitations of traditional unscented Kalman filter estimation methods, to accurately estimate the state of charge (SOC) of power battery, a method for SOC estimation of power battery based on unscented Kalman particle filter was proposed. Taking the ternary lithium battery as research object, the second-order RC equivalent circuit model of the battery was established to identify the model parameters through the battery charging and discharging test, and the accuracy of the model was verified. The battery data under actual working conditions were collected, and the SOC was estimated by untracked Kalman filter algorithm, particle filter algorithm and untracked Kalman particle filter algorithm, respectively. The simulation experiments were carried out in MATLAB, and the estimated SOC values were compared. The results show that the untracked Kalman particle filter algorithm can estimate the SOC quickly and accurately, and the error is less than 2.5%, which is better than those of other two algorithms. [ABSTRACT FROM AUTHOR]

We derive a generalization of the Kalman filter that allows for non‐Gaussian background and observation errors. The Gaussian assumption is replaced by considering that the errors come from a mixed distribution of Gaussian, lognormal, and reverse lognormal random variables. We detail the derivation for reverse lognormal errors and extend the results to mixed distributions, where the number of Gaussian, lognormal, and reverse lognormal state variables can change dynamically every analysis time. We test the dynamical mixed Kalman filter robustly on two different systems based on the Lorenz 1963 model, and demonstrate that non‐Gaussian techniques generally improve the analysis skill if the observations are sparse and uncertain, compared with the Gaussian Kalman filter. [ABSTRACT FROM AUTHOR]

This paper proposes a Kalman filter for linear rectangular singular discrete-time systems, where the singular matrix in the system is a rectangular matrix without full column rank. By using two different restricted equivalent transformation methods and adding the measurement equation to the state equation, the system is transformed into a square singular system satisfying regularity and observability. During this process, the causality of the system is taken into account, and multiple matrix transformations are applied accordingly. Based on these modifications, state estimation results are obtained using the Kalman filter. Finally, a numerical example is employed to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Short-term traffic flow forecasting, an essential enabler for intelligent transportation systems, is a fundamental and challenging task for dramatically changing traffic flow over time. In this paper, we present a gravitational search optimized kernel extreme learning machine, named GSA-KELM, to avoid manually traversing all possible parameters to improve the potential performance. Furthermore, with the interference of heavy-tailed impulse noise, the performance of KELM may be seriously deteriorated. Based on the Kalman filter that cleverly combines observed data and estimated data to perform the closed-loop management of errors and limit the errors within a certain range, we propose a combined model, termed GSA-KELM-KF. The experimental results of two real-world datasets demonstrate that GSA-KELM-KF outperforms the state-of-the-art parametric and non-parametric models. [ABSTRACT FROM AUTHOR]

Some inverse problems of Stokes flow, including noisy boundary conditions, unknown angular velocity, and dynamic viscous constant identification are studied in this paper. The interpolation equations for those inverse problems are constructed using the method of fundamental solutions (MFS). Based on the noise addition technique, the inverse problems are solved using MFS and a Kalman filter. It is seen from numerical experiments that these approaches and algorithms are valid and have strong robustness and high accuracy in solving inverse Stokes problems. [ABSTRACT FROM AUTHOR]

Rail freight is a common mode of transportation for bulk commodities and is widely used around the world, with heavy quality and large inertia. Conventional control methods are influenced by the dynamic parameters of the train model. To solve this problem, this paper designs a mayfly algorithm-predictive active disturbance rejection controller (MA-PADRC) for speed tracking and wheel anti-slip control of electric freight trains. This control method reduces dependence on accurate modeling. MA-PADRC integrates the searching ability of MA, predicting ability of Smith estimator and high efficiency of ADRC, and it can accurately estimate the driving disturbance of the freight train. Meanwhile, to prevent wheel slip, an Elman neural network-Kalman filter (ENN-KF) is designed. Through it, the precise observation of anti-slip parameters and the constraint of anti-slip control are realized. The proposed control is validated by simulation of both real and virtual driving speeds. The results show that the proposed control has a strong tracking performance, and can effectively prevent wheel slip and guarantee the safe driving of the freight train with large inertia. [ABSTRACT FROM AUTHOR]

The power system network is complex and diverse in nature because of the random and irregular existence of renewable sources, various types of varying and constant loads. Under these circumstances, the stabilization of DC link voltage and obtaining reduced power fluctuations are the main control aspect of DC microgrid (MG) system. This paper explores a Kalman filter (KF) based voltage–current control algorithm for the battery unit to control the medium voltage direct current (MVDC) bus voltage accurately with trivial oscillations as compared to the traditional VI algorithm. The stability and monitoring of DC microgrids are greatly influenced by gathering sufficient and precise information. Since installing several sensors on DC MGs is costly and increases DC MG ripple. In this work, an extended Kalman filter (KF) is used to estimate the current of the lead-acid battery. Hence the current represents an important factor in guaranteeing safe operations. Where the Kalman Filter, which improves the estimation efficiency, to reach a good performance in the current prediction. By updating the proposed approach provides smooth and noise-less operation from the measured DC voltage. The control strategy established for electric vehicle (EV) in this paper is to improve the overall charging ability of the EV only if the system's total power is greater than the load level by means of the current loop management technique. The feasibility of the designed MG system with the established battery management mechanism and the Escape Ford EV performance is studied using MATLAB/Simulink platform. [ABSTRACT FROM AUTHOR]

Electroanatomical mapping is a method for creating a model of the electrophysiology of the human heart. Medical professionals routinely locate and ablate the site of origin of cardiac arrhythmias with invasive catheterization. Non-invasive localization takes the form of electrocardiographic (ECG) or magnetocardiographic (MCG) imaging, where the goal is to reconstruct the electrical activity of the human heart. Non-invasive alternatives to catheter electroanatomical mapping would reduce patients' risks and open new venues for treatment planning and prevention. This work introduces a new system state-based method for estimating the electrical activity of the human heart from MCG measurements. Our model enables arbitrary propagation paths and velocities. A Kalman filter optimally estimates the current densities under the given measurements and model parameters. In an outer optimization loop, these model parameters are then optimized via gradient descent. This paper aims to establish the foundation for future research by providing a detailed mathematical explanation of the algorithm. We demonstrate the feasibility of our method through a simplified one-layer simulation. Our results show that the algorithm can learn the propagation paths from the magnetic measurements. A threshold-based segmentation into healthy and pathological tissue yields a DICE score of 0.84, a recall of 0.77, and a precision of 0.93. [ABSTRACT FROM AUTHOR]

The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition. [ABSTRACT FROM AUTHOR]