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Hybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems. [ABSTRACT FROM AUTHOR]

Summary The long‐range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy‐intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy‐efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter‐based ADR (KF‐ADR) and moving median‐based ADR (Median‐ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. [ABSTRACT FROM AUTHOR]

We investigate the optimal investment-and-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques, we convert the original optimization problem involving different filtrations into an equivalent stochastic control problem under the observation filtration, i.e. the so-called separated problem. The Markovian structure of the separated problem allows us to apply a classical approach to stochastic optimization based on the Hamilton–Jacobi–Bellman equation, and to provide explicit formulas for the value function and the optimal investment-and-reinsurance strategy. We finally discuss some comparisons between the optimal strategies pursued by a partially informed insurer and that followed by a fully informed insurer, and we evaluate the value of information using the idea of indifference pricing. These results are also supported by numerical experiments. [ABSTRACT FROM AUTHOR]

Summary Kalman smoother is an effective algorithm to estimate the state of the dynamic systems with Gaussian noise. However, when the system is affected by non‐Gaussian noise, the traditional Kalman smoother may suffer severe performance degradation, since it is derived from the minimum mean square error criterion. By introducing the maximum correntropy criterion, which accounts for all higher order moments and has the ability to resist non‐Gaussian noise, this article studies the state estimation problem of the bilinear state‐space system with non‐Gaussian noises and parametric uncertainties. The bilinear system with parametric uncertainties is transformed into a linear time‐varying system, and a robust fixed‐point Kalman filter algorithm is derived based on the Cauchy kernel‐based correntropy criterion. To improve the state estimation accuracy, a Cauchy kernel‐based fixed‐point Kalman smoother (CK‐FPKS) algorithm is presented by introducing the backward smoothing. Simulation results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the last decade, spoofing attack has been known as the most dangerous intentional interference in Global Positioning System (GPS). This work suggested an algorithm that performs spoofing mitigation by utilising the Kalman filter. In particular, first, in the tracking stage multi-correlator structure is established and correlators output are investigated by using a Multi-Layer Perceptron Neural Network (MLP NN) in other to detect the spoofing attack, after that, the Kalman filter estimates the DLL discriminator output. The proposed method was verified using three different spoofing in which, the mitigation rate was 84.25%, 93.20% and 97.24%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a novel power management strategy (PMS) is proposed for optimal real-time power distribution between battery and supercapacitor hybrid energy storage system in a DC microgrid. The DC-bus voltage regulation and battery life expansion are the main control objectives. Contrary to the previous works that tried to reduce the battery current magnitude and its fluctuation to increase the battery life, the battery thermal considerations, which are a key factor in battery life, are also considered as an objective function in the proposed PMS. Also, the quick charging capability of the battery is another objective in the optimization problem. To solve this multi-objective problem, the Karush–Kuhn–Tucker conditions have been used to make the PMS suitable for real-time operation. In addition, one of the important inputs of conventional PMS is the amount of grid excess/deficiency power. Due to the use of the adaptive Kalman filter estimator, another superior of the proposed PMS is using local data and no need for communication and knowledge of the load or generation power. Using simulation, the performance of the proposed method is compared with three traditional methods filter-based, droop control, and fuzzy logic in terms of controlling the battery current, battery temperature, and battery health, and its superiority has been shown. Finally, the validation of the proposed PMS is experimentally tested on a real setup. [ABSTRACT FROM AUTHOR]

The paper presents the reconstruction of sea levels in the North Sea and Baltic Sea using Kalman filter approach. Based on the statistical characteristics of one year of daily maps of sea level from the Geesthacht COAstal model SysTem (GCOAST) and daily data at tide gauges along the coastline of two basins, the method can reconstruct effectively and accurately the multidecadal sea level anomalies. The high accuracy reconstruction data were then used to investigate the interannual variability in both basins and to estimate the difference between outflows and inflows (net flux) through the Danish Straits. The highest mean, standard deviation, and extreme values of sea level anomalies appear in winter and are well reproduced in different regions, such as the German Bight, the Southern North Sea, the Bothnian Bay, the Gulfs of Finland and Riga. The sea level variability is highly correlated with the mean sea level pressure and the zonal wind, particularly in the German Bight and in the winter months. The contributions of river runoff and net precipitation on the net flux are significant in the spring. The local wind has a greater influence on the net flux than the remote drivers. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are essential for a wide range of applications, including environmental monitoring and smart city developments, thanks to their ability to collect and transmit diverse physical and environmental data. The nature of WSNs, coupled with the variability and noise sensitivity of cost-effective sensors, presents significant challenges in achieving accurate data analysis and anomaly detection. To address these issues, this paper presents a new framework, called Online Adaptive Kalman Filtering (OAKF), specifically designed for real-time anomaly detection within WSNs. This framework stands out by dynamically adjusting the filtering parameters and anomaly detection threshold in response to live data, ensuring accurate and reliable anomaly identification amidst sensor noise and environmental changes. By highlighting computational efficiency and scalability, the OAKF framework is optimized for use in resource-constrained sensor nodes. Validation on different WSN dataset sizes confirmed its effectiveness, showing 95.4% accuracy in reducing false positives and negatives as well as achieving a processing time of 0.008 s per sample. [ABSTRACT FROM AUTHOR]

Underwater autonomous operation is becoming increasingly crucial as a means to escape the hazardous high-pressure deep-sea environment. As a result, it is essential for there to be underwater exploration. The development of sophisticated computer vision is the single most significant factor for the success of underwater autonomous operations. In order to improve low-quality photos and compensate for low-light circumstances, preprocessing is used in underwater vision. This allows for clearer pictures to be seen. In this paper, we propose a deep convolutional neural network (DCNN) method for solving the weakly illuminated problem for underwater pictures. This method combines the max-RGB and shade-of-grey approaches to improve underwater visibility and to train the plotting association necessary to obtain the lighting plot. Using this method, we are able to resolve the problematic of weakly illuminated pictures in a way that is efficient. After the photos have been prepared, a deep convolutional neural network (DCNN) approach is developed for detection and classification in the water. Two updated methods are then utilized in order to adapt the architecture of the DCNN to the qualities of underwater vision. The purpose of this investigation is to present a Kalman Filter (KF) method as a solution to the difficulties associated with underwater communication in terms of object tracking and detection. We were able to separate a section of the object by employing a threshold segment and morphological technique. This allowed us to investigate the invariant moment and area properties of the section. Based on the findings, it can be decided that the suggested technique is useful for monitoring underwater targets using DCNN-KF. Furthermore, it displays high resilience, high accuracy, and real-time characteristics. Results from the simulations show that the suggested model DCNN-KF does a better job of localization than the most advanced methods at the time of the study. [ABSTRACT FROM AUTHOR]

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node's estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost. [ABSTRACT FROM AUTHOR]

Some of the extreme space weather events like the geomagnetic storm of 17–18 March 2015 have produced dramatic effects on low-latitude ionosphere as reported in various studies. Similarly, some of the extreme storms are studied to emphasize the anomalous deviations in ionospheric delay; however, the effects of excess ionospheric delay on GPS-based positioning are rarely reported from India. This paper presents a robust analysis including estimation of GPS receiver position during 6 major geomagnetic storms using dual frequency GPS signals in L1 and L2 bands. Epoch-wise solution estimates are computed using precise orbit SP3 files for satellite orbit and clock corrections and receiver specific differential code biases are obtained from IGS-CODE center. A range domain Kalman filter has also been developed to smooth the 30 s sampled code pseudorange using carrier phase data and resolve the initial ambiguity. Then, the precise positioning residuals are estimated in two different cases of ionospheric corrected and ionospheric uncorrected error models. We then compare the results with ionospheric corrected position estimates to finally obtain the effect of excess daytime ionospheric variations during main phase of some extreme geomagnetic storms. Results from 5 GPS receivers located within 77°–80° E longitude sector are obtained for two 2-hour windows covering local noon during 6 storms including the St. Patrick's Day storm. The effect of severity of the storms and their impact on static precise positioning are brought out by comparing the results with performance on a quiet day. It is found that the magnitude of error in estimated altitude exhibits maximum deviations due to ionospheric variations during storms, and the dip latitude of station is important in terms of magnitude of ionospheric error in positioning over the equatorial ionization anomaly region. [ABSTRACT FROM AUTHOR]

This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform. • A novel, comprehensive periodic event-triggered networked control solution is proposed. • High reduction of resource usage (of up to 93%) can be reached while maintaining acceptable control performance. • A non-uniform dual-rate extended Kalman filter (NUDREKF) is essential in the control structure. • Some conditions are provided such that the prediction error of the NUDREKF is exponentially mean square bounded. • Real path-following control tests for a holonomic Mecanum-wheeled robot validate the control solution. [ABSTRACT FROM AUTHOR]

A real-time system may be subjected to various anomalies that can affect the quality of the observations. The main motivation of the article arises from the need in addressing challenges posed by the presence of anomalies in discrete linear time-invariant (LTI) systems with a focus on the estimation processes, in the context of position and temperature measurements. The proposed approach leverages the properties of discrete LTI systems and takes advantage of the predictive capabilities of the moving horizon strategy (MHS). It operates recursively updating estimates of new measurement while fairly considering its past estimates that occur within the window of the moving horizon. The estimation framework will be designed to handle disturbances and provide robust estimates, to ensure the effectiveness of the system. In order to validate the proposed approach simulation studies were conducted on different and only scenarios in different order LTI system. Comparative studies with different estimation techniques demonstrate the capability of the proposed approach in terms of performance and efficiency. The proposed approach can be applied to systems with changing system dynamics. Future research may be conducted to utilize this strategy in other domains to mitigate anomalies while enhancing performance. [ABSTRACT FROM AUTHOR]

This research aims to explore the potential of image metrology as a substitute for monitoring building structural deformations. It utilizes stereo-photogrammetry and the Kalman filter in its approach. The ultimate goal is to reduce the damage to human lives and property caused by the collapse of building structures in urban areas. Image metrology and conventional geodetic surveying were both used in monitoring the deformation of a selected building prone to land subsidence. Four geodetic monitoring stations were established using the GNSS surveying technique, while 10 photo points were placed on the selected building for deformation monitoring. Simultaneous observation of photo points and acquisition of their images were carried out during the first three months of this study. The data acquired via geodetic survey were subjected to least square adjustment while the images acquired were subjected to stereophotogrammetry and Kalman filtering for extraction and refinement of photo point coordinates. The preliminary results show that image metrology is comparable to conventional geodetic survey methods for monitoring building deformation down to 100 mm. The t statistic value of 1.524234 and t critical value of 1.7291333 justify the comparability. [ABSTRACT FROM AUTHOR]

Vehicle tracking is one of computer vision's most important applications, with applications ranging from robotics and traffic monitoring to autonomous vehicle navigation and many more. Even with the significant advancements in recent research, issues like occlusion, fluctuating illumination, and fast motion still need to be addressed, calling for more investigation and creativity in this field. This study performs a thorough examination of various vehicle-tracking approaches and suggests a thorough classification scheme that divides them into four main categories: strategies that rely on features, segmentation, estimate, or learning. Two wellknown methods are highlighted specifically in the estimation-based category: particle filters and Kalman filters. [ABSTRACT FROM AUTHOR]

Enabling rapid and accurate comprehensive environmental perception for vehicles poses a major challenge. Object detection and monocular distance estimation are the two main technologies, though they are often used separately. Thus, it is necessary to strengthen and optimize the interaction between them. Vehicle motion or object occlusions can cause sudden variations in the positions or sizes of detection boxes within temporal data, leading to fluctuations in distance estimates. So, we propose a method to integrate a detector based on YOLOv5-RedeCa, a Bot-Sort tracker and an anomaly jumping change filter. This combination allows for more accurate detection and tracking of objects. The anomaly jump filter smooths distance variations caused by sudden changes in detection box sizes. Our method increases accuracy while reducing computational demands, showing outstanding performance on several datasets. Notably, on the KITTI dataset, the standard deviation of the continuous ranging results remains consistently low, especially in scenarios with multiple object occlusions or disappearances. These results validate our method's effectiveness and precision in managing dual tasks. [ABSTRACT FROM AUTHOR]

This paper introduces a non-contact method for detecting contact force between pantograph and catenary (PAC), based on image processing and Kalman filter. A non-contact method for detecting contact force of PAC, which only utilizes the vertical displacement of the collector head, is proposed. First, the images of the collector head area are collected by adding an image acquisition system on the roof of the vehicle. Second, combined with target tracking and template matching, the vertical displacement of the pantograph's collector head is identified. Finally, based on the Kalman filter considering least squares method and the PAC dynamics model, a method for detecting contact force is constructed. The estimation of the PAC's dynamic contact force is realized only by using the vertical displacement of the collector head obtained from the image identification. The results of simulation and test-bed results show that the Kalman filter considering least squares significantly has higher accuracy and better robustness compared with the traditional Kalman filter. [ABSTRACT FROM AUTHOR]

To enable real‐time monitoring and control strategies for floating offshore wind turbines, accurate information about the state of the system is needed. This paper details the application of a Kalman filter to the UMaine VolturnUS‐S floating wind platform to provide accurate state estimates in real time using minimal system measurements. The midfidelity nonlinear simulation tool OpenFAST was used to generate the underlying linear state‐space model for the Kalman filter. This linear model and its limitations are demonstrated through comparison with experimental data collected on a 1:70 froude‐scaled model of the floating platform and tower. Using a selection of five measurements from the real system, a Kalman filter was developed to provide estimates for the remaining system states and measurements. These estimates were then validated against the experimental values collected from testing of the scale model. Validation of the Kalman filter produced accurate estimates of surge, heave, and tower base bending moment, measurements of which were not available to the Kalman filter. Performance of the Kalman filter was tested and validated over a range of sea conditions from rated wind speed to storm events and demonstrated robustness in the Kalman filter to maintain accuracy across all operating conditions despite significant error in the underlying linear model for extreme conditions. [ABSTRACT FROM AUTHOR]

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads. [ABSTRACT FROM AUTHOR]

The output processer of interval type-2 fuzzy logic systems (IT2FLSs) is a complex operator which performs type-reduction plus defuzzification (TR+D) tasks. In this paper, a complexity-reduced yet high-performance TR+D for IT2FLSs based on Maclaurin series approximation is utilized within a feedback-error-learning (FEL) control structure for controlling linear move stages. IT2FLSs are widely used for control purposes, as they provide extra degrees of freedom to increase control accuracies. FEL benefits from a classical controller, which is responsible for providing overall system stability, as well as a guideline for the training mechanism for IT2FLSs. The Kalman filter approach is utilized to tune IT2FLS parameters in this FEL structure. The proposed control method is applied to a linear stage in real time. Using an identification process, a model of the real-time linear stage is developed. Simulation results indicate that the proposed FEL approach using the Kalman filter as an estimator is an effective approach that outperforms the gradient descent-based FEL method and the proportional derivative (PD) classical controller. Motivated by the performance of the proposed Kalman filter-based FEL approach, it is used to control a linear move stage in real time. The position feedback of the move stage is provided by a precision laser interferometer capable of performing measurements with an accuracy of less than 1   μ m . Using this measurement system in a feedback loop with the proposed control algorithm, the overall steady state of the system is less than 20   μ m . The results illustrate the high-precision control capability of the proposed controller in real-time. [ABSTRACT FROM AUTHOR]

This study introduces a novel methodology integrating computer vision, visual servo control, and the Kalman Filter to precisely estimate object locations for a 3-RRR planar type parallel manipulator. Through kinematic analysis and the development of a vision system using color indicators, the research enhances the ability of the manipulator to track object trajectories, especially in cases of occlusion. Employing Eye-to-Hand visual servo control, the research further refines the visual orientation of the sensor for optimal end effector and object identification. The incorporation of the Kalman Filter as a robust estimator for occluded objects underscores the predictive accuracy of the system. Results demonstrate the effectiveness of the methodology in trajectory generation and object tracking, with potential implications for improving robotic manipulators in dynamic environments. This comprehensive approach not only advances the fields of kinematic control and visual servoing but also opens new avenues for future research in complex spatial manipulations. [ABSTRACT FROM AUTHOR]

AbstractThis paper explores the exponential stochastic volatility model generated by first-order exponential Markov sequences to model financial time series. The stationary exponential Markov sequences used to generate the volatility model are the linear exponential autoregressive model, minification model, new exponential autoregressive time series model, and an exponential mixture of autoregressive and minification processes. The statistical properties of the models are studied and the estimation of model parameters is carried out using the generalized method of moments. An approximate Kalman filtering method and a non-linear filtering approach are employed to diagnose the models. To verify the correctness of the estimations, simulation studies are conducted, and two real datasets are used to demonstrate how the models are applied. Furthermore, a comparison of the suggested models is performed using the measures root mean square error, mean absolute error, and Value at Risk via a backtesting procedure. [ABSTRACT FROM AUTHOR]

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium- ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation. [ABSTRACT FROM AUTHOR]

Numerical weather forecast models have biases caused by insufficient grid resolution and incomplete physical processes, especially near the land surface. Therefore, the Japan Meteorological Agency (JMA) has been operationally post-processing the forecast model outputs to correct biases. The operational post-processing method uses a Kalman filter (KF) algorithm for surface temperature prediction. Recent reports have shown that deep convolutional neural networks (CNNs) outperform the JMA operational method in correcting temperature forecast biases. This study combined the CNN-based bias correction scheme with the JMA operational KF algorithm. We expected that the combination of CNNs and a KF would improve the post-processing performance, as the CNNs modify large horizontal structures, and then, the KF corrects minor spatiotemporal deviations. As expected, we confirmed that the combination outperformed both CNNs and the KF alone. This study demonstrated the advantages of the new method in correcting coastal fronts, heat waves, and radiative cooling biases. [ABSTRACT FROM AUTHOR]

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a "core–subcore–periphery" emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. [ABSTRACT FROM AUTHOR]

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite's position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method's potential for practical applications. [ABSTRACT FROM AUTHOR]

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance. • A sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed to achieve fading-free sensing. [ABSTRACT FROM AUTHOR]

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ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory, which was designed for rapid assimilative runs. ANCHOR uses recently developed PyIRI model for the background and for the formation of the background covariance matrix. It only takes a few minutes for ANCHOR to complete the data assimilation (DA) for one day, including data pre‐processing and model set up. ANCHOR extracts ionospheric parameters from radio occultation (RO) and ionosonde data using PyIRI formalism and assimilates them as point measurements into maps of the background parameters using a Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment with synthetic data simulated using the SAMI3 model. ANCHOR reduces the root mean square errors in the analysis by more than a half for all of the ionospheric parameters in comparison to the background. Finally, this paper discusses advantages and limitations of the parametrized ionospheric DA, highlighting the avenues for its future improvement. Plain Language Summary: ANCHOR is a novel data assimilation model developed at the U.S. Naval Research Laboratory. It combines estimates of the electron density in the Earth's ionosphere from climatological model with observations, producing a better model representation that matches the data. It extracts ionospheric parameters from radio occultation and ionosonde data and assimilates them as point measurements into the maps of the background parameters using Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment. Key Points: Novel approach for rapid ionospheric data assimilation (DA) using anchor pointsGlobal ionospheric DA for plasma density parametersExtraction of the anchor points from the ionospheric data [ABSTRACT FROM AUTHOR]

The methodology presented in this work allows for the creation of a real-time adjustment of Kalman Filter process noise for lithium battery state-of-charge estimation. This work innovates by creating a methodology for adjusting the process ( Q ) and measurement ( R ) Kalman Filter noise matrices in real-time. The filter algorithm with this adaptative mechanism achieved an average accuracy of 99.56% in real tests by comparing the estimated battery voltage and measured battery voltage. A cell-balancing strategy was also implemented, capable of guaranteeing the safety and efficiency of the battery pack in all conducted tests. This work presents all the methods, equations, and simulations necessary for the development of a battery management system and applies the system in a practical, real environment. The battery management system hardware and firmware were developed, evaluated, and validated on a battery pack with eight LiFePO4 cells, achieving excellent performance on all conducted tests. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot's position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot's position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research. [ABSTRACT FROM AUTHOR]

A procedure is outlined aiming at testing the bias due to omitted variables in vector autoregressions. The procedure consists first of filtering a vector of omitted variables and then testing the bias. The test does not rely on the availability of the omitted variables, and is based on a comparison between maximum-likelihood with Kalman filter vector autoregression and linear vector autoregression estimates. The empirical part considers two illustrative examples: a univariate regression analysis, based on the rational expectation-augmented Phillips curve; and a VAR with output, inflation and interest rates where a "price puzzle" arises. [ABSTRACT FROM AUTHOR]

Safety-critical navigation systems often involve multiple sensor types, including global navigation satellite system (GNSS), to enhance positioning accuracy. Additionally, the systems' reliability has been significantly improved through the application of fault detection and exclusion (FDE) techniques. An enhanced AIME (Autonomous Integrity Monitoring by Extrapolation) method is introduced in our approach to identify faulty satellites. This is accomplished by analyzing the measured Kalman filter (KF) innovations and their covariances within a sliding window. Furthermore, a strategy for fault separation, detection, and exclusion is developed for GNSS and IMU, making use of their relationship within the KF innovation vector derived from the GNSS measurement model. Similarly, the innovation vector derived from the visual measurement model is employed to detect visual faults, with the assumption that the inertial measuring unit (IMU) is fault-free. Upon detecting faults, we proceed to redesign the system noise matrix and measurement noise matrix within the KF using test statistics, effectively excluding measurement faults from satellites and IMU. In order to assess the performance of our proposed method, we conducted a field test utilizing the collected vehicle-mounted dataset. The results demonstrate the effectiveness of our FDE method in accurately identifying faulty satellites, detecting IMU faults of varying magnitudes, and excluding abnormal visual observations. Furthermore, after fault exclusion, the maximum position error during the fault time period decreased by an average of 62%. [ABSTRACT FROM AUTHOR]

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R² = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management. [ABSTRACT FROM AUTHOR]

Laser powder bed fusion (LPBF) is an additive manufacturing technology with high practical value. In order to improve the quality of the fabricated parts, process monitoring has become a crucial solution, offering the potential to ensure manufacturing stability and repeatability. However, a cardinal challenge involves discerning a precise correlation between process characteristics and potential defects. This paper elucidates the integration of an off-axis vision monitoring mechanism via a high-speed camera focused on capturing the single-track melting phenomenon. An innovative image processing method was devised to segment the plume and spatters, while Kalman filter was employed for multi-object tracking of the spatters. The features of both the plume and spatters were extracted, and their relationship with molten states was investigated. Finally, the PSO-XGBoost algorithm was utilized to identify five molten states, achieving an accuracy of 92.16%. The novelty of this approach resides in its unique combination of plume characteristics, spatter features, and computationally efficient machine learning models, which collectively address the challenge of limited field of view prevalent in real production scenarios, thereby enhancing process monitoring efficacy. Relative to existing methodologies, the proposed PSO-XGBoost approach offers heightened accuracy, convenience, and appropriateness for the monitoring of the LPBF process. This work provides an effective and novel approach to monitor the LPBF process and evaluate the part fabrication quality for complex and changeable working conditions. [ABSTRACT FROM AUTHOR]

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation. [ABSTRACT FROM AUTHOR]

The estimation of spatiotemporal data from limited sensor measurements is a required task across many scientific disciplines. In this paper, we consider the use of mobile sensors for estimating spatiotemporal data via Kalman filtering. The sensor selection problem, which aims to optimize the placement of sensors, leverages innovations in greedy algorithms and low-rank subspace projection to provide model-free, data-driven estimates. Alternatively, Kalman filter estimation balances model-based information and sparsely observed measurements to collectively make better estimation with limited sensors. It is especially important with mobile sensors to utilize historical measurements. We show that mobile sensing along dynamic trajectories can achieve the equivalent performance of a larger number of stationary sensors, with performance gains related to three distinct timescales: (i) the timescale of the spatiotemporal dynamics, (ii) the velocity of the sensors, and (iii) the rate of sampling. Taken together, these timescales strongly influence how well-conditioned the estimation task is. We draw connections between the Kalman filter performance and the observability of the state space model and propose a greedy path planning algorithm based on minimizing the condition number of the observability matrix. This approach has better scalability and computational efficiency compared to previous works. Through a series of examples of increasing complexity, we show that mobile sensing along our paths improves Kalman filter performance in terms of better limiting estimation and faster convergence. Moreover, it is particularly effective for spatiotemporal data that contain spatially localized structures, whose features are captured along dynamic trajectories. [ABSTRACT FROM AUTHOR]

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]

In recent years, there has been a lot of interest in the estimation of missing frames in video sequences because of its usage in several critical applications such as medical imaging, manufacturing, security systems, etc. The present study employs the Kalman filtering approach to suggest a technique for estimating missing frames within a video sequence and forecasting forthcoming frames. The Kalman filter model proposed in this study employs a pixel-by-pixel approach to leverage information from both preceding and subsequent video frames for the purpose of predicting the missing frame. The experimental findings demonstrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation of the direction and distance of sound sources with the MUSIC beamforming algorithm, and their tracking with the help of Kalman filter. Direction-of-arrival (DOA) estimations can be performed using a combination of acoustical focusing and beamforming. Distance estimation is usually not part of the process, but it is possible through an extension of the beamforming algorithm. MUSIC (Multiple Signal Classification) is a relatively fast and simple method to locate sound sources. It is based on the separation of the received signals’ cross-spectral matrix to signal and noise subspaces. We also use the Kalman filter and its extended non-linear version to track moving sound sources. We evaluate the performance of these methods through simulations in the MATLAB environment and measurements with unmanned aerial vehicles (UAV). DOA estimations and tracking are possible in both cases, but distance estimation is significantly more problematic in the latter. We aim to find the cause of the errors in the estimation during measurements, to develop a more robust method in the future. [ABSTRACT FROM AUTHOR]

This paper developed a new and efficient approach, the metaheuristic optimisation approach based on the Kalman filter (KF), to accurately identify the infinite impulse response (IIR) system coefficients. A recently developed metaheuristic optimisation technique called the manta-ray foraging optimisation (MRFO) algorithm has been employed to solve the proper parameters tuning exertion of KF. The problem of achieving better unknown state estimations by the conventional KF is closely connected with decent prior information about the noise statistics (measurement and process noise). However, the statistics mentioned above depend on the types of applications and process dynamics. The use of insufficiently known priori filter statistics would reduce the precision of the estimated filter or sometimes lead to a practical divergence of the filter. To alleviate this problem, the proposed technique initially produces optimally tuned KF parameters using the MRFO algorithm, and then the conventional KF method estimates the coefficients of the IIR model using the optimised KF parameters obtained at the beginning. The simulation studies have been carried out on three distinct order benchmark IIR systems (both same and reduced-order), and two prominent IIR applications called direct current (DC) motor and fractional order digital differentiator (FODD) using the proposed method. The overall identification results demonstrate that the proposed MRFO-KF technique significantly improves performance metrics over competing techniques, such as basic KF, other employed metaheuristic-based KF schemes and recently reported state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios. [ABSTRACT FROM AUTHOR]

The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion. [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]

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]

To protect civilian Global Positioning System (GPS) users from spoofing, the Air Force Research Lab has developed the chips-message robust authentication (Chimera) signal enhancement for the GPS L1C signal. With Chimera, standalone receivers that only have access to the GPS signal will be able to authenticate their received measurements once every 3 min, whereas users with access to an out-of-band source will be able to perform authentication once every 1.5 or 6 s. However, moving receivers typically rely on much faster real-time GPS update rates of 1-20 Hz. In this work, we design a spoofing-resilient filter framework that provides continuous and secure state estimation between Chimera authentication times. By leveraging self-contained sensors on-board the vehicle, such as an inertial measurement unit or wheel encoder, as well as the periodic Chimera authentication, our proposed filter determines how much to rely on the received unauthenticated GPS measurements for state estimation. In this respect, our filter relies more extensively on GPS measurements in order to improve real-time navigation performance and reduce localization errors when GPS signals are authentic, while successfully mitigating spoofing-induced errors during an experienced attack. We experimentally validate our proposed spoofing-resilient filter in a simulated test environment for a ground vehicle model with access to the 3-min Chimera channel, under various simulated spoofing attack scenarios. To the best of the authors' knowledge, this is the first adaptive filter proposed for Chimera that continuously leverages real-time GPS measurements in a spoofing-resilient manner. [ABSTRACT FROM AUTHOR]

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states' variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional (3D) localization within indoor environments is crucial for enhancing item-based application services, yet current systems often struggle with localization accuracy and height estimation. This study introduces an advanced 3D localization system that integrates updated ultra-wideband (UWB) sensors and dual barometric pressure (BMP) sensors. Utilizing three fixed UWB anchors, the system employs geometric modeling and Kalman filtering for precise tag 3D spatial localization. Building on our previous research on indoor height measurement with dual BMP sensors, the proposed system demonstrates significant improvements in data processing speed and stability. Our enhancements include a new geometric localization model and an optimized Kalman filtering algorithm, which are validated by a high-precision motion capture system. The results show that the localization error is significantly reduced, with height accuracy of approximately ±0.05 m, and the Root Mean Square Error (RMSE) of the 3D localization system reaches 0.0740 m. The system offers expanded locatable space and faster data output rates, delivering reliable performance that supports advanced applications requiring detailed 3D indoor localization. [ABSTRACT FROM AUTHOR]