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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]

Thermal issue has been regarded as one of the bottlenecks for GaN high-electron-mobility transistor (HEMT) performance and reliability, which highlights the importance of accurate thermal modeling. In the present work, we propose a GP (Gaussian process)-resistor–capacitor compact thermal model integrated with the ensemble Kalman filter (EnKF) to handle the nonlinear problems attributed to the temperature-dependent properties of GaN HEMTs under large-signal working conditions. The GP predictor is employed for the nonlinear correction term, with strong ability and extendibility to characterize various temperature-dependent relations resulting from different design configurations and materials. The model is identified via the EnKFs by inputting a sequence of channel temperature oscillations induced by imposing a large-signal continuous wave heating source to the device. Furthermore, an adaptation mode is devised for the in situ and timely update of the model parameters to adapt to the thermal variability of GaN devices, avoiding storing a large amount of historical data and repeated offline regressions. The validation of our modeling scheme is conducted through the case study on GaN-on-SiC HEMT's detailed 3D finite element method simulations. [ABSTRACT FROM AUTHOR]

Chatter in the milling system can result in poor surface quality of the workpiece, low machining efficiency, damage to the tool, and so on. Owing to the limitations of the control position and the complexity of the processing environment, existing methods exhibit several shortcomings in the design and comprehension of the control system. Therefore, an optimal active chatter control method is proposed in this paper; the control weight parameters, input ways, and state prediction effects of the controller are explored in depth. First, a new active control device with adjustable control positions is designed utilizing piezoelectric actuators. The control system is expected to achieve good performance for the chatter induced by the flexibility of the milling tool. Then based on the presented chatter control system, a model predictive control (MPC) algorithm is used to calculate the optimal control force. In addition, the Kalman filter is applied to predict the optimal state feedback value, based on which an optimal active chatter control strategy is proposed. Simulation results indicate that the saturation problem of the actuator can be solved effectively by using incremental input control. The Kalman filter is a state observer that can predict the response of the system accurately. Finally, some milling experiments are carried out utilizing the designed active control device. The experimental results prove that the proposed optimal control method can suppress the milling chatter effectively. [ABSTRACT FROM AUTHOR]

Time‐varying group formation tracking control problems for multi‐agent systems are investigated based on distributed multi‐sensor multi‐target filtering with intermittent observations. First, in order to estimate the states of multiple targets under the phenomenon of intermittent observations accurately, a distributed multi‐sensor multi‐target filtering algorithm is proposed based on cubature Kalman filtering. Second, a time‐varying group formation tracking protocol is designed for multi‐agent systems by using the state estimations obtained from the filtering algorithm and the neighboring interaction. The protocol enables multi‐agent systems to form time‐varying subformations and track multiple targets in the same subgroups, respectively. Third, the boundedness of the error covariance matrices is proved under the condition that the observation probability is higher than the minimum threshold. Then the estimation errors of the filtering algorithm are proved to be stochastically bounded by introducing a stochastic process. Furthermore, the boundedness of the group formation tracking errors is proved. Finally, a numerical example is used to verify the performance of the proposed algorithm and protocol. [ABSTRACT FROM AUTHOR]

The limited ensemble sizes and the neglected model errors in ensemble-based Data Assimilation (DA) usually lead to underestimation of the error variance, and covariance inflation 'inflates' the covariance of a forecast or analysis ensemble by some positive factor in each assimilation cycle. In practice, the value of the inflation factor is often set by trial and error. Thus, a variety of adaptive covariance inflation (ACI) algorithms have been proposed to improve assimilation performance by adjusting the optimal parameters. Most of these used traditional optimization algorithms, such as gradient methods and relaxation methods. However, Evolutionary Algorithms(EAs), which encompass a range of population-based stochastic search techniques widely used in various engineering optimization problems, are rarely seen in DA. In this study, we developed a new framework for adaptive inflation factors, introducing differential evolution (DE) algorithm into local ensemble Transform Kalman filter (LETKF) that can explore the most suitable/optimal inflation factors online. A 'micro-DE' based data assimilation method can obtain better assimilation results step by step without taking a much longer time. The feasibility and effectiveness of the algorithm is demonstrated in an idealized Lorenz-96 model with simulated observations. Under both perfect and imperfect-model scenarios, it is found that the 'micro-DE'LETKF method is capable of outperforming the original LETKF with a considerable lower computational cost. The adaptive inflation factor is optimally updated at each assimilation cycle either around the upper bound or around the lower bound, which means that only an adaptive factor helps mitigate the ensemble collapse due to a lack of spread. However, the further study will consider the application of the methodology to more complex atmospheric models. [ABSTRACT FROM AUTHOR]

Displacement is an important parameter for evaluating structural performance. However, the accurate measurement of global dynamic displacement remains a challenging task. To solve this problem, this paper proposes a global dynamic displacement reconstruction method for lattice tower structures, fusing limited acceleration and strain data. First, the strain-displacement mapping method for cantilever beams with variable cross-sections is presented and then extended to lattice towers. Thereafter, a modified multi-rate data fusion algorithm incorporating Kalman filtering and the proposed strain-displacement mapping methods is developed to fuse the acceleration and strain data to reconstruct the global dynamic displacement of the tower. The numerical simulation, model test and full-scale test demonstrate that the reconstructed dynamic displacements using the proposed method agree well with the reference values in both the time and frequency domains, and the parametric analysis has also been carried out, exhibiting great robustness and high reconstruction accuracy. [ABSTRACT FROM AUTHOR]

The real-time and accurate detection and ranging of ships play a pivotal role in ensuring navigation safety, this study aims to enhance the navigation safety and environmental perception capabilities of inland waterway vessels. In the ship detection stage, addressing challenges such as large parameters, high computational complexity, and poor real-time performance in existing ship detection models, this paper proposes the MS-YOLOv5s ship target detection algorithm. This algorithm, based on YOLOv5s, utilizes the lightweight MobileNetV3-Small network to replace the original YOLOv5s feature extraction backbone network, thereby improving the detection speed. The results indicate that the parameter size of the MS-YOLOv5s model is 3.55M, only 50.49% of YOLOv5s. Achieving a detection rate of 50.28 FPS, the precision is 96.80%, and the mAP is 98.40%, striking a balance between high accuracy and low computational demand. In the depth estimation stage, influenced by the environment, leading to unstable measurement data, this paper proposes a binocular Kalman filter fusion ranging algorithm. The standard deviation of the ranging results is minimized to 6.032μm, which is one order of magnitude smaller than traditional ranging algorithms, significantly enhancing the robustness of the measurement results. Within a distance of 20m from the ship target, the error can be controlled within 3%, showcasing the applicability of the method proposed in this paper in complex inland waterway environments contributes to the enhancement of ships' environmental perception capabilities and navigation safety, holding positive implications for the development of intelligent vessels. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution. [ABSTRACT FROM AUTHOR]

AA6082 alloys play a significant role in advancing sustainable development goals (SDGs) by contributing to environmental sustainability, economic growth, and social well-being. These alloys are highly recyclable and align with SDG 12 by promoting resource efficiency and reducing waste. Their application in lightweight vehicles and improving energy efficiency in construction supports SDG 9 and SDG 11, as they help reduce carbon emissions and enhance the sustainability of urban environments. While AA6082 alloys offer significant advantages, their use has limitations that can hinder their industrial applications. One key challenge is their lower formability, particularly at room temperature. Elevated-temperature deformation is frequently employed to enhance the formability of these alloys and address their limitations. Thus, a deep understanding of the constitutive analysis of these alloys under a wide range of T and ε ˙ is essential for manufacturing sound components from these alloys. Thus, this study aims to propose a new modification for the JC model (PJCM) and compare its reliability to predict the warm/hot flow behavior of AA6082 alloys with that of the original JC model (OJCM) and the modified JC model (LMJCM). By comparing the experimental results with these model results and confirming the determining correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE) values, it is concluded that the stresses predicted by the PMJCM closely match the experimental stresses of the LMJCM and OJCM because of the interaction between ε ˙ , ε , and T , which might be a reason for the complex nonlinear behavior of AA6082 alloys during hot deformation. [ABSTRACT FROM AUTHOR]

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system's reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40 × 10 − 15 and 2.30 × 10 − 15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively. [ABSTRACT FROM AUTHOR]

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach. [ABSTRACT FROM AUTHOR]

The availability of Global Navigation Satellite System (GNSS) raw observations in smartphones has driven research into low-cost GNSS solutions, especially in challenging urban environments, which have garnered significant attention from scholars in recent years. This study proposes an improved smartphone-based velocity-aided positioning method and conducts vehicle-mounted experiments in urban roads representing typical scenarios. The results show that when transitioning from low- to high-multipath environments, the number of visible satellites and carrier phase observations are highly sensitive to environmental factors, with frequent multipath effects. The introduction of robust pre-fit and post-fit residual algorithms has proven to be an effective quality control method. Additionally, using more refined observation models and appropriate parameter estimation algorithms led to a slight 6% improvement in velocity performance. The improved Kalman filter position estimation model (KFSPP-P) strategy, by incorporating velocity uncertainty into the state estimation process, overcomes the limitations of conventional velocity-aided smartphone positioning methods (KFSPP-V) in complex urban environments. In low-multipath environments, the accuracy of the KFSPP-P strategy is comparable to that of KFSPP-V, with an approximate 8% improvement in horizontal accuracy. However, in more challenging environments, such as tree-lined roads and urban environments, the KFSPP-P strategy shows significant improvements, particularly enhancing horizontal positioning accuracy by approximately 50%. These advancements demonstrate the potential of using smartphones to provide reliable positioning services in complex urban environments. [ABSTRACT FROM AUTHOR]

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent. [ABSTRACT FROM AUTHOR]

Continuous nanohertz gravitational waves from individual supermassive black hole binaries may be detectable with pulsar timing arrays. A novel search strategy is developed, wherein intrinsic achromatic spin wandering is tracked simultaneously with the modulation induced by a single gravitational wave source in the pulse times of arrival. A two-step inference procedure is applied within a state-space framework, such that the modulation is tracked with a Kalman filter, which then provides a likelihood for nested sampling. The procedure estimates the static parameters in the problem, such as the sky position of the source, without fitting for ensemble-averaged statistics such as the power spectral density of the timing noise, and therefore complements traditional parameter estimation methods. It also returns the Bayes factor relating a model with a single gravitational wave source to one without, complementing traditional detection methods. It is shown via astrophysically representative software injections in Gaussian measurement noise that the procedure distinguishes a gravitational wave from pure noise down to a characteristic wave strain of |$h_0 \approx 2 \times 10^{-15}$|⁠. Full posterior distributions of model parameters are recovered and tested for accuracy. There is a bias of |$\approx 0.3$| rad in the marginalized one-dimensional posterior for the orbital inclination |$\iota$|⁠ , introduced by dropping the so-called pulsar terms. Smaller biases |$\lesssim 10~{{\ \rm per\ cent}}$| are also observed in other static parameters. [ABSTRACT FROM AUTHOR]

Monitoring seismic velocity changes obtained from ambient noise correlations is widely used to understand changes in rock properties in response to earthquakes, volcanic activities and environmental changes. Since continuous seismic data have been accumulated, this method can estimate long-term changes in seismic velocity, such as crustal recovery after a major earthquake and temporal variations in seismic velocity related to long-term environmental change. Changes in seismic velocity can be estimated with a high temporal resolution by measuring the phase differences of ambient noise correlations based on a seismic interferometry method. Still, these phase differences are influenced not only by seismic wave velocity changes but also by errors in clock timing in seismometers. The clock drift occurs due to out-of-synchronization with the GPS clock and the drift of the internal clock. Therefore, to accurately monitor temporal changes in crustal structure by measuring the phase differences of noise correlations, it is crucial to evaluate the contribution of errors in clock timing to the phase differences. Recently, a method using an extended Kalman filter based on a state-space model was developed for reliable detection of temporal changes in the waveforms of ambient noise correlations, with the state-space model offering the advantage of flexible modelling of time-series data. In this study, we incorporated the time-shifts caused by clock time errors of the seismometer into the state-space model of the temporal changes in ambient noise correlations. We estimated seismic velocity changes, amplitude changes of noise correlations and clock time errors from 2010 April to 2021 September at seismic stations around the Shinmoe-dake volcano in Japan, which experienced eruptions in 2011 and 2018, respectively. Several stations exhibited clear clock time offsets, and the occurrence of clock time-shifts coincided with the dates when the data logger was turned off for seismic station maintenance or replacement of the seismometer. The proposed method provides stable estimations with respect to the signal-to-noise ratio of the waveform, and this stable estimation facilitates accurate timing of seismic recordings, enabling precise analysis of seismic phase arrival times. [ABSTRACT FROM AUTHOR]

Adjusting the sensitivity-weighting matrix, which is a key parameter affecting the filtering accuracy in the desensitized Kalman filter (DKF), is still an open problem. To address this issue, a new adaptive fast DKF (AFDKF) algorithm and adaptive fast desensitized extended Kalman filter (AFDEKF) have been proposed. The fast filters have an adaptive factor that enables them to adjust the sensitivity-weighting matrix based on the orthogonality principle of measurement residuals. This adaptive factor is calculated by using the corresponding process and measurement information. Then, a new desensitized cost function with an adaptive factor is designed. An analytical gain is obtained by minimizing this cost function to reduce computation cost. The performance of the AFDKF and AFDEKF algorithms are demonstrated using two numerical examples. [ABSTRACT FROM AUTHOR]

Orbit determination using optical survey measurements for cataloging spacecraft encounters new obstacles from mega-constellations. These challenges arise from the sparse observations, and the frequent occurrences of unknown maneuvers which are difficult to model using traditional methods. By utilizing 14 Fourier coefficients, the Thrust-Fourier-Coefficients (TFC) model can effectively represent the averaged variations of orbital elements caused by arbitrary maneuvers. However, this model contains systematic offsets that will reduce the accuracy of orbit determination, and is unsuitable for spacecraft in nearly circular orbits. This paper first replaces the classical Keplerian elements of the previous TFC model with the nonsingular elements. Then detailed analytical expressions for model offsets have been derived based on Kozai's orbit-averaging method. A process noise covariance matrix related to the model offsets has been added to the iterated extended Kalman filter to compensate for the unmodeled dynamics and enhance the accuracy. Simulations based on this modified method demonstrate that the accuracy of orbit determination for maneuvering satellites can meet the requirements of catalog maintenance even with sparse observations. • A filtering-through approach is developed for orbit determination. • Thrust-Fourier-Coefficients gives equivalent trajectory of maneuvering target. • Nonsingular elements are used for spacecraft in nearly circular orbits. • Analytical expressions of dynamic model offset are derived by orbit-averaging method. • Adding process noise with model offset improves orbit determination accuracy. [ABSTRACT FROM AUTHOR]

In this work, we present a novel design for an embedded Radio Frequency (RF) antenna array that can distinguish various materials by analyzing changes in Received Signal Strength Indicator (RSSI) values. The use of a low-cost and small-form-factor microcontroller by Espressif makes this design both cost-effective and suitable for integration into various applications, differentiating it from previous studies. To enhance the material classification performance, a combination of Kalman filter and Support Vector Machine is proposed which does not require a large amount of training data for model optimization. Results demonstrate that the proposed machine learning model is able to perform material classification within a 2 m range, with an average accuracy of over 96%. Such a system is well-suited for intelligent mobile robotic applications particularly in warehouse automation or smart manufacturing lines due to its ability for proximal remote sensing, real-time monitoring, and multimodal sensing. [ABSTRACT FROM AUTHOR]

This paper examines empirically cross‐fertilization in the productivity growth of banks between a state and its neighbouring and non‐neighbouring states (i) before (i.e. 1971–1977) the interstate multibank holding company (IMBHC) deregulations and (ii) during (i.e. 1982–1995) the IMBHC deregulations, which, through cross‐border bank M&As mainly among neighbouring states, could inject new blood, awaken the market for corporate control and enhance cross‐fertilization in bank performance among neighbouring states. Further, the 1978–1981 period offers a natural experiment to examine Baumol's Contestable Markets Hypothesis (CMH). The legislature of Maine made the first IMBHC deregulatory move in 1978. There was no reciprocity until New York and Alaska made their moves in 1982. Under CMH, Maine's move should inject a competitive spirit and alter bank performance for better across all—neighbouring or non‐neighbouring – banking markets during this period. Theoretically, Kane's regulatory equilibrium framework provides guidance to address these matters and Tiebout's people vote with their feet framework extends and supplements this guidance. Empirically, FDIC's annual banking data, aggregated at the state level, constitute the main input in computing the productivity growth indices for each of the 48 contiguous sample states between 1971 and 1995. Estimations of a novel spatially driven fixed effects model that uses these indices produce empirical results. The empirical model exploits the proximity of one sample state to its neighbouring states while also embracing a set of randomly chosen non‐neighbouring states as a control sample. Results show that cross‐fertilization in bank performance, observed among neighbouring states before the introduction of the IMBHC deregulations during 1971–1977, gets stronger in response to the dynamically evolving IMBHC deregulations during 1982–1995 and that improvements in banks' productivity growth during 1978–1981 support Baumol's CMH. Overall, our results demonstrate the importance and influence of cross‐fertilization, as a matter of proximity of subjects, on banks' performance and suggest promise for future research that embraces the spatial dimension of banking markets and data. [ABSTRACT FROM AUTHOR]

The Kalman filter and extended Kalman filter algorithm are widely used in physical education video processing, but their performance still needs to be improved for complex backgrounds or high dynamic targets. This paper presents an interactive multi-model algorithm. The displacement detection Kalman filter is used to track moving objects. This method can solve the problem of a single model not being able to match the motion features well. Finally, the simulation experiment of football match video proves that the proposed method can significantly improve the tracking accuracy of moving objects in video. [ABSTRACT FROM AUTHOR]

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies. [ABSTRACT FROM AUTHOR]

This paper primarily focuses on designing stealthy false data injection attacks targeting two communication channels in cyber‐physical systems equipped with state estimators and attack detectors. It introduces the concept of perfect attacks, rendering the attack detector unable to detect the designed attack signals, thereby enabling attackers to further destabilize the system. Through the error model designed by the attacker, the designed attack does not require knowledge of the state estimator. By injecting a carefully designed perfect attack into the communication channel, it is possible to extend the state estimation error of a residual detector system based on the Kalman filter to infinity. Moreover, this designed attack can bypass the residual‐based detector, thus achieving a completely stealthy attack. Finally, the results of theoretical derivations are validated by a numerical example and a practical case. [ABSTRACT FROM AUTHOR]

This article addresses the Bayesian filtering problem for a class of nonlinear systems under multistep randomly delayed and lost measurements. A new measurement model is established that can characterize the random delay and loss of measurement data. First, an augmented Gaussian mixture filter framework is developed in the case of random delay of measurement data; the posterior probability density function after state augmentation is calculated by marginalizing over delay variables to extract accurate information from delayed measurements. The implementation of the filter is transformed into the computation of nonlinear numerical integrals. Second, under the proposed framework, novel expressions of the mean and covariance are generated by propagating the measurement taken at the previous moment in the event of no new measurement being received. Finally, we present two simulation examples for estimating system states, and the results demonstrate the effectiveness and superiority of our proposed filter. [ABSTRACT FROM AUTHOR]

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation. [ABSTRACT FROM AUTHOR]

When a ship–bridge collision occurs, prompt assessment of substructure damage is crucial. This study presents a novel approach for ship–bridge collision damage identification, addressing challenges inherent in traditional monitoring systems. The method overcomes issues such as complex installation, low efficiency, and high costs through a unique combination of the unscented Kalman filter (UKF) and computer vision technique. The approach exerts the structural equation of motion to derive a multirate UKF in the impact process, thereby identifying the stiffness of structures. Displacement and acceleration are fused to enhance the sampling rate of vision-measured displacement. Firstly, it monitors low sampling rate displacements on piers using computer vision, complemented by high-rate accelerometer data at the collision point. Secondly, displacement and acceleration data are integrated using a multirate UKF, addressing the challenge of image storage pressure associated with vision-based measurements. Finally, validation using finite-element and experimental models confirms the effectiveness of the approach in identifying substructure stiffness and recovering lost vibration characteristics. In experiment validation, the influence of computer vision algorithms and camera shooting distance on displacement monitoring and stiffness identification is also discussed separately. This approach provides a cost-effective and efficient solution for ship–bridge collision damage identification, contributing to advancements in the field of ship–bridge collision monitoring. [ABSTRACT FROM AUTHOR]

The steel–concrete hybrid tower (SCHT) has been popularized as the supporting structure for onshore wind turbines to meet the increasing hub height and rotor diameter. The internal force of the cross-section, which is the prerequisite of the structural design, is based on the load analysis. However, the nonignorable effect of the second-order gravity makes analyzing the external load of the supporting tower a coupled problem, especially for the SCHT, which is higher than a conventional steel tubular tower. Besides, the aerodynamic load of the wind turbine, the main excitation acting on the supporting tower, is usually computed by numerical methods and lack of validation. To optimize the analysis method for the internal force of the SCHT, one feasible approach is evaluating the external excitation directly. Based on the theory of the Kalman filter, this study develops a dynamic inverse method of the SCHT. It aims to estimate the horizontal thrust of the wind turbine and provide an overall evaluation of the dynamic responses of the SCHT by using the measured dynamic responses at the limited point on the SCHT. A rigorous numerical simulation based on ABAQUS is conducted to generate the dynamic responses of the SCHT, which are used to validate the effectiveness of the proposed method. It also investigated the influence of observation type, number of measuring points, and noise level on the estimation accuracy. [ABSTRACT FROM AUTHOR]

Owing to rapid advancements in information and communication technology, natural language processing (NLP) methods have attracted considerable attention. Estimating uncertainty is a well‐established problem in NLP. Developing systems capable of automatically detecting uncertain terms within a natural language is critical for developing effective text‐analysis algorithms. Consequently, several NLP tools have been developed for this purpose. However, there are several obstacles to the design of fast and effective NLP technologies that effectively analyze ambiguous terms in natural languages. This work focuses on a particular method for detecting uncertainty, termed progressive detection, which can be accomplished using a dynamic probabilistic graphical model to solve these challenges. We iteratively upgraded the Kalman Filter to Labeled Variable Dimension Kalman Filter (LVDKF) based on observations from real datasets. The LVDKF learns two basic features from positive and negative words. The formula was then derived using a dynamic detection system, designed based on conditional probability. Finally, we simulated progressive detection experimental conditions and assessed the LVDKF on two publicly available datasets. It performs better than the baseline methods in these tests, which shows that it is effective at detecting changes over time. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we propose an adaptive Kalman filtering with adaptive window length based on Q‐learning for dynamic systems with unknown model information. The iteration step length of the Q‐function is quantitatively adjusted through the influence function. The adaptive Kalman filtering algorithm is used to set an appropriate weight matrix for the Q‐function to estimate unknown model parameters. One numerical example and a practice‐oriented case are given to illustrate the effectiveness of the proposed method. It is shown that this filtering can provide state estimates of best accuracy among all the compared methods when the model mismatch and noise statistical characteristics change. [ABSTRACT FROM AUTHOR]

ABSTRACT The Kalman filter provides an optimal estimation for a linear system with Gaussian noise. However, when the noises are non‐Gaussian in nature, its performance deteriorates rapidly. For non‐Gaussian noises, maximum correntropy Kalman filter (MCKF) is developed which provides a more accurate result. In a scenario, where the actual system model differs from nominal consideration, the performance of the MCKF degrades. For such cases, in this article, we have proposed a new robust filtering technique for a linear system which maximizes a cost function defined by exponential of weighted past and present errors weighted with the kernel bandwidth. During filtering, at each time step, the kernel bandwidth is selected by maximizing the correntropy function of error. Further, a convergence condition of the proposed algorithm is derived. Numerical examples are presented to show the usefulness of the proposed filtering technique. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to enhance and compare different neural network (NN)-based side-slip angle estimators. The feed-forward neural networks (FFNNs), recurrent neural networks, long short-term memory units (LSTMs), and gated recurrent units are investigated. However, there is a lack in the selection criteria of the architectures' hyper-parameters. Therefore, the genetic algorithm is integrated with the NN-based estimators to find the optimal hyper-parameters for the studied architectures. The tuned hyper-parameters in this work include the number of neurons, number of layers, activation function, optimizer type, and learning rate. The objective function of the optimization problem is minimizing the root-mean-square error (RMSE) on multiple testing data. The optimal models are further included in the design of a hybrid NN estimator with Kalman filter. In the hybrid estimators, the optimal NN estimators are used as virtual sensors to correct the prediction of the side-slip angle resulting from the mathematical lateral vehicle model. Eventually, the performance of the best selected model is evaluated in terms of different metrics; mean RMSE, mean error variance, mean training time, and mean estimation time. LSTMs are found to achieve the lowest mean RMSE while being tested on highly generalized data yielding the highest training and estimation time. However, FFNNs achieve the lowest RMSE while being tested on low generalized data and the lowest training and estimation time. Meanwhile, it is observed that the hybrid estimators achieved lower RMSE with great enhancement compared to the non-hybridized ones proving the effectiveness of the proposed approach and increasing the side-slip estimation generalization ability in unknown environments with high uncertainties, which are not covered by the training dataset for the NNs estimators. [ABSTRACT FROM AUTHOR]

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system. [ABSTRACT FROM AUTHOR]

ABSTRACT This study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs). [ABSTRACT FROM AUTHOR]

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios. [ABSTRACT FROM AUTHOR]

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands. [ABSTRACT FROM AUTHOR]

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target's illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains. [ABSTRACT FROM AUTHOR]

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters. Our approach involves the integration of neural networks within the filtering algorithms, enabling the dynamic augmentation of parameters in response to performance feedback. We present two modified filters: a neural network-based Kalman filter and an alpha-beta filter, each augmented to incorporate neural network-driven parameter tuning. The alpha-beta filter is enhanced with neural network outputs for its α and β parameters, while the Kalman filter employs a neural network to optimize its internal parameter R and noise factor F. We assess these advanced models using the root mean square error (RMSE) metric, where our neural network-based alpha-beta filter demonstrates a significant 38.2% improvement in prediction accuracy, and the neural network-based Kalman filter achieves a 53.4% enhancement. Hence, our novel approach of integrating neural networks into filtering algorithms stands out as an effective strategy to significantly enhance their performance in dynamic environments. [ABSTRACT FROM AUTHOR]

This paper proposes a novel reliable model predictive control (MPC) method for active vibration control of structure with nonprobabilistic uncertainties. First, the framework of reliable MPC is established by integrating nonprobabilistic reliability constraints into nominal MPC. Based on the first‐order Taylor expansion and first‐passage theory, an efficient nonprobabilistic reliability analysis method that is suitable for online computation is proposed. A nonprobabilistic Kalman filter is further proposed for determine system states and their uncertain region. Unlike most robust MPC approaches, the proposed reliable MPC focuses on the satisfaction of state constraints in terms of structural reliability and is more suitable for structures with stringent safety requirements. Compared to existing reliability‐based vibration control methods, reliable MPC requires no knowledge of disturbance and exhibits greater adaptability to load environments. The effectiveness and superiority of the proposed reliable MPC are validated through a numerical example and an engineering case study. [ABSTRACT FROM AUTHOR]

In this paper we study optimal trading strategies in a financial market in which stock returns depend on a hidden Gaussian mean reverting drift process. Investors obtain information on that drift by observing stock returns. Moreover, expert opinions in the form of signals about the current state of the drift arriving at fixed and known dates are included in the analysis. Drift estimates are based on Kalman filter techniques. They are used to transform a power utility maximization problem under partial information into an optimization problem under full information where the state variable is the filter of the drift. The dynamic programming equation for this problem is studied and closed-form solutions for the value function and the optimal trading strategy of an investor are derived. They allow to quantify the monetary value of information delivered by the expert opinions. We illustrate our theoretical findings by results of extensive numerical experiments. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS)/Inertial Navigation System (INS) integration is a widely used technique for navigation and positioning applications. It combines the advantages of GPS and INS to provide accurate and reliable information. However, the GPS/INS integration suffers from performance degradation during a GPS outage, which occurs when natural or artificial factors block the GPS signal. The novelty of this paper is improving GPS/INS integration performance during GPS outages using Incremental Regularized LSTM (IncRLSTM) learning. Incremental learning is a learning paradigm that can be learned from streaming data online and updating the model parameters without forgetting the previous ones. Also, regularization is a technique that prevents overfitting and improves the generalization of the network by adding some constraints or penalties to the model. IncRLSTM learning models the GPS signal as a multi-objective regression and corrects the INS output with the Faded Memory Kalman filter. The results on real-world datasets significantly show the reduction of positioning errors by an average of 72 % during GPS outages and the improvement of the accuracy and robustness of GPS/INS integration by an average of 58 % compared with existing methods. Moreover, IncRLSTM presents, on average, a 10 % improvement compared to the existing methods. [ABSTRACT FROM AUTHOR]

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery's lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery's lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources. [ABSTRACT FROM AUTHOR]

With the rapid advancement of mobile robotics technology, Simultaneous Localization and Mapping (SLAM) has become indispensable for enabling robots to autonomously navigate and construct maps of unknown environments in real time. Traditional SLAM algorithms, such as the Extended Kalman Filter (EKF) and FastSLAM, have shown commendable performance in certain applications. However, they encounter significant limitations when dealing with nonlinear systems and non-Gaussian noise distributions, especially in dynamic and complex environments coupled with high computational complexity. To address these challenges, this study proposes an enhanced particle filtering method leveraging particle swarm optimization (PSO) to improve the accuracy of pose estimation and the efficacy of map construction in SLAM algorithms. We begin by elucidating the foundational principles of FastSLAM and its critical role in empowering robots with the ability to autonomously explore and map unknown territories. Subsequently, we delve into the innovative integration of PSO with FastSLAM, highlighting our novel approach of designing a bespoke fitness function tailored to enhance the distribution of particles. This innovation is pivotal in mitigating the degradation issues associated with particle filtering, thereby significantly improving the estimation accuracy and robustness of the SLAM solution in various operational scenarios. A series of simulation experiments and tests were conducted to substantiate the efficacy of the proposed method across diverse environments. The experimental outcomes demonstrate that, compared to the standard particle filtering algorithm, the PSO-enhanced particle filtering effectively mitigates the issue of particle degeneration, ensuring reliable and accurate SLAM performance even in challenging, unknown environments. [ABSTRACT FROM AUTHOR]

The loose integration system of high-rate GNSS and strong-motion records based on Kalman filtering technology is currently a research focus for capturing broadband co-seismic displacements. To address the problem of time-varying system noise variance in the standard Kalman filter (SKF), a variance compensation adaptive Kalman filter (VC-AKF) was adopted in this study to obtain more accurate high-precision broadband co-seismic displacement and provide reliable data support for seismic scientific research and practical applications. The algorithm continuously updates the system noise variance and calculates the state vector by collecting prediction residuals in real time. To verify the effectiveness and superiority of this method, a numerical simulation and a seismic experiment from the 2017 Ms 7.0 Jiuzhaigou earthquake were carried out for comparative analysis. Based on the simulation results, the precision of the proposed algorithm was 46% higher than that of the SKF. The seismic experiment results indicate that the proposed VC-AKF approach can eliminate the baseline shift of accelerometers and weaken the influence of time-varying system noise variance towards more robust displacement information. [ABSTRACT FROM AUTHOR]

This study proposes a comprehensive methodology for accurate State of Charge (SOC) estimation in lithium-ion batteries by optimizing equivalent circuit model (ECM) parameters under varying temperature conditions using the Taguchi method. Analysis of Variance (ANOVA) was employed to evaluate the influence of these parameters on ECM accuracy. Experiments were conducted at −10 °C, 25 °C, and 40 °C to evaluate the effects of pulse time gap, discharge pulse time, and C-rate on SOC estimation accuracy. A genetic algorithm-based multi-objective optimization technique was employed to minimize RMSE in the extended Kalman filter (EKF) SOC estimation process. The results showed that temperature significantly impacts SOC prediction, with deviations most pronounced at low (−10 °C) and high (40 °C) temperatures. When assessments are conducted for different SOC levels (SOC90, SOC50, SOC30), the key results highlight the substantial influence of pulse time gap and discharge pulse time on model accuracy. Also, it was observed that there is a significant reduction in RMSE, indicating improved performance under optimized conditions. The findings are particularly relevant for real-time applications, such as electric vehicles, where accurate SOC estimation is crucial for battery management. [ABSTRACT FROM AUTHOR]

In the control of unipolar sinusoidal excited switched reluctance motors (SRMs), deadbeat predictive current controllers (DPCCs) have gained attention for their enhanced dynamic performance. However, periodic disturbances caused by mismatches between the predictive model's nominal and actual system parameters degrade the control performance of SRMs. To address this issue, a robust DPCC with multi-parameter compensation is proposed to improve dq0-axes current control performance. By analyzing the impact of parameter mismatches, a Kalman filter (KF) is developed to compensate for inductance coefficient mismatches, mitigating periodic disturbances. Additionally, a disturbance estimator with measurement noise suppression is integrated into the DPCC for both state and disturbance estimation to handle residual uncertainties, including winding resistance mismatches, magnetic saturation, and unmodeled dynamics. Compared simulation and experimental results validate the effectiveness of the proposed robust DPCC. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional spatiotemporal distribution information on near-surface aerosols is of great significance for environmental research. In this study, a 3D scanning coaxial Mie lidar (3D-STML) was developed to achieve a fast three-dimensional scanning observation of aerosol diffusion processes in near-surface areas. 3D-STML generates high-spatiotemporal resolution images of aerosol extinction coefficient in real-time and captures the dynamic changes of aerosols in near real-time. By optimizing the design of the light guide mirror and the telescope sub-mirror, the system has a small overlap. Based on this, a highly stable and high-speed mechanical rotation mechanism was developed to enable three-dimensional observations. The integration of a solid-state high-repetition-rate pulsed laser and a coaxial, optical system for the transmitter and receiver ensures rapid tracking of aerosol plumes. To meet the observation requirements of near-surface aerosols, an aerosol inversion algorithm combining the Fernald and Klett methods was designed and developed. For aerosol plume monitoring needs, an aerosol plume-tracking algorithm based on Kalman filtering was developed to track the spatiotemporal evolution of aerosols automatically. Experimental results demonstrated that 3D-STML is capable of detecting aerosols in a range from 15 m to 4 km, with a distance resolution of 1.5 m and a time resolution of 0.083 s. It can effectively track and capture aerosol plumes. It can be used for large-scale, long-term observation of near-surface aerosols and for monitoring the spatiotemporal evolution of aerosol plumes. [ABSTRACT FROM AUTHOR]

Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long‐range correlations resulting from the limited‐size ensembles imposed by computational burden constraints. Such ad‐hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one‐year‐long ensemble experiments with a fully realistic EnKF‐DA system in the Red Sea using tens ‐to thousands of ensemble members. The system assimilates satellite and in‐situ observations and accounts for model uncertainties by integrating a 4‐km‐resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long‐range correlations produced by the low‐rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non‐Gaussianity generated by the perturbed internal physical parameterization schemes. Large‐ensemble forcing fields and non‐Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA. [ABSTRACT FROM AUTHOR]

Weather centres use a variety of data assimilation schemes to analyze different land variables in their operational forecast systems. Current activities at the European Centre for Medium‐Range Weather Forecasts (ECMWF) are working towards a unified and more consistent land data assimilation system to provide more accurate initial conditions for the atmospheric forecasts. The first step is to replace the current 1D optimal interpolation (1D‐OI) used for first‐layer soil and snow temperature analyses, and integrate multi‐layer soil and first‐layer snow temperature into the ensemble‐based simplified extended Kalman filter (SEKF) currently used for multi‐layer soil moisture. This work focuses on the technical developments and the evaluation of the atmospheric forecast skill of a series of numerical weather prediction experiments to compare different SEKF configurations with the former 1D‐OI over a three‐month summer and winter period. Using the SEKF leads to seasonally varying significant improvements in the 2‐m temperature forecast in the verification against own analyses and to slightly improved results in the validation using independent synoptic observations. This work lays the foundation for integrating additional land variables into the SEKF and investigating stronger land–atmosphere coupling. [ABSTRACT FROM AUTHOR]