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Fast Anomaly Detection and Joint State Estimation for Perturbed Nonlinear Systems With Prolonged Output Anomalies 认领 引用
1
作者 Guangdeng Chen Xiao-Jie Peng +2 位作者 Yan Lei Chao Huang Hongyi Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1301-1313,共13页
State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems,with its difficulty further exacerbated by potential network attacks.This study investigates fast a... State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems,with its difficulty further exacerbated by potential network attacks.This study investigates fast anomaly detection and state estimation for perturbed nonlinear systems where actual outputs may be anomalous over a prolonged period.First,a fixedtime observer is constructed.By leveraging integral-type composite Lyapunov functions and homogeneity theory,the error bounds are proven under varying scenarios involving model disturbances,measurement noise,and nonlinearity.Based on these bounds,a fast anomaly detection mechanism is designed.Next,a cascade predictor is developed based on the fixed-time observer,which uses historical outputs from a previous time window to predict the current system state.Simultaneously,an algorithm is proposed to determine the reference historical output based on anomaly detection results,improving long-term prediction accuracy and mitigating the impact of anomaly detection delays.Finally,the secure state estimation is derived by fusing states from the fixed-time observer and the cascade predictor,depending on the anomaly detection results.The effectiveness of the proposed method is demonstrated through simulations on autonomous vehicles. 展开更多
关键词 Anomaly detection cascade predictor fixed-time observer output anomalies secure state estimation
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Mixed-state Berry curvature in quantum multiparameter estimations 认领 引用
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作者 Xiaoguang Wang Xiao-Ming Lu +2 位作者 Yunbo Zhang Libin Fu Shu Chen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第5期80-84,共5页
For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-st... For pure states,the quantum Berry curvature has been well studied.However,the quantum curvature for mixed states has received less attention.From the concept of symmetric logarithmic derivative,we introduce a mixed-state quantum curvature and find that it plays a key role in the field of multi-parameter precision estimations.Through spectral decomposition,we derive the mixed-state Berry curvature for both the full-rank and non-full-rank density matrices.As an example,we obtain the exact expression of the Berry curvature for an arbitrary qubit state. 展开更多
关键词 Berry curvature Uhlmann curvature mixed states quantum parameter estimation
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State estimation improvement in strongly coupled data assimilation with a two-scale Lorenz model 认领 引用
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作者 Yanqiu Gao Shaoting Ge +2 位作者 Jicai Zhang Yiting Wang Qun He 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2026年第1期176-189,共14页
Coupled data assimilation(CDA)is a powerful strategy for integrating observations with coupled numerical models.This strategy holds great potential for enhancing weather and climate reanalysis and prediction.How to ad... Coupled data assimilation(CDA)is a powerful strategy for integrating observations with coupled numerical models.This strategy holds great potential for enhancing weather and climate reanalysis and prediction.How to address crossscale interactions in CDA is an important issue.In particular,the cross-scale interactions in the strongly coupled data assimilation(SCDA)framework pose substantial challenges.In this study,increasing the state estimation accuracy using an ensemble adjustment Kalman filter based on the two-scale Lorenz’96(tsL96)model is investigated.Using the SCDA framework,we adopt cross-component localization factors and several covariance inflation schemes to address the filter divergence problem.The results show that ensembles of an appropriate size can achieve good assimilation results,the optimal localization parameters are scale-dependent for the model variables,and the adaptive inflation scheme outperforms the static fixed and relaxation-to-prior spread schemes.Although these experiments were carried out using an ideal framework,this study provides a valuable reference for improving estimation accuracy with the SCDA framework in operational simulation and prediction models. 展开更多
关键词 state estimation ensemble adjustment Kalman filter strongly coupled data assimilation multiple-scale
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State Estimation With Model Uncertainty Using Structure Variational Bayesian and Transfer Learning 认领 引用
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作者 Shuang Gao Xiaoli Luan +2 位作者 Biao Huang Shunyi Zhao Fei Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期715-727,共13页
This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliab... This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliable,and the target domain model has significant model parameter uncertainties.To enhance estimation performance in the target domain,the proposed method transfers model knowledge from the source domain and adjusts it using a tuning factor before incorporating it into the target domain estimator.More specifically,this approach involves transferring the modified probability density functions of state prediction from the source domain to the target domain and determining the tuning factor via structure variational Bayesian inference using measurements in the target domain.Using numerical examples and a 1-DOF torsion system,we showcase the competitiveness of the proposed state estimator compared to the existing robust state estimation methods when dealing with parameter uncertainties.The results highlight its capability to improve estimation accuracy in practical scenarios,showcasing its potential for real-world applications. 展开更多
关键词 Markovian jump linear systems parameter uncertainty state estimation structure variational inference transfer learning
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A Robust Hybrid WLS-EKF Algorithm for Power System State Estimation 认领 引用
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作者 Zahid Javid Kush Lohana +1 位作者 Danial Murtaza William Holderbaum 《Energy Engineering》 EI 2026年第7期525-547,共23页
This paper introduces a novel hybrid method for Power System State Estimation(PS-SE)that effectively integrates the strengths of Weighted Least Squares(WLS)and the Extended Kalman Filter(EKF)through an adaptive weight... This paper introduces a novel hybrid method for Power System State Estimation(PS-SE)that effectively integrates the strengths of Weighted Least Squares(WLS)and the Extended Kalman Filter(EKF)through an adaptive weighting mechanism.The proposed method addresses key challenges in modern PS-SE,including measurement uncertainties,bad data detection and handling,and convergence reliability.By incorporating an adaptive weighting mechanism,the hybrid approach dynamically adjusts estimation parameters based on the quality of the measurements,enabling it to maintain high accuracy for clean data while demonstrating exceptional resilience against outliers and noisy measurements.The performance of the proposed method is rigorously evaluated against established state estimation techniques,including WLS,EKF,Bayesian method,Huber-Adaptive Method(HAM)and Neural network-based Method.Simulations are performed on IEEE 14-bus,IEEE 34-bus and IEEE 342-bus test systems to assess estimation accuracy,convergence behavior,computational efficiency,and robustness in the presence of bad data.Results highlight the superior performance of the hybrid method,which achieves higher accuracy and robust convergence properties while requiring 40%fewer iterations than conventional WLS.Despite its enhanced capabilities,the computational burden remains comparable to traditional techniques,making it highly suitable for real-time applications.These findings underscore the proposed hybrid method as a significant advancement in power system state estimation,offering a reliable,efficient,and robust solution for modern power system monitoring and control.It represents a promising approach to address the increasing complexity and data uncertainties in contemporary power grids. 展开更多
关键词 Adaptive estimation hybrid estimator power system monitoring state estimation
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Multi-area distributed three-phase state estimation for unbalanced active distribution networks 认领 引用 被引量：7
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作者 Sheng CHEN Zhinong WEI +3 位作者 Guoqiang SUN Ning LU Yonghui SUN Ying ZHU 《Journal of Modern Power Systems and Clean Energy》 SCIE EI 2017年第5期767-776,共10页
This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric c... This paper proposes a new multi-area framework for unbalanced active distribution network(ADN) state estimation. Firstly, an innovative three-phase distributed generator(DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs,together with the states of alternating current(AC) buses in ADNs, were estimated by using the weighted least squares(WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN.Finally, from the simulation results on the modified IEEE123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed. 展开更多
关键词 Active distribution networks(ADNs) Distributed three-phase state estimation Three-phase distributed generator(DG)models Pseudo-measurements Augmented Lagrangian
DGMSE:A Real-Time Dynamic Object Removal Framework Based on Detection-Guided Markov State Estimation 认领 引用
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作者 Jiahua Kou Chengbo Guo +4 位作者 Weiyue Xing Zheng Yang Jiaxuan Cao Shufa Sun Yanling Guo 《Computers, Materials & Continua》 SCIE EI 2026年第9期1442-1472,共31页
Dynamic objects in LiDAR SLAM often introduce ghosting artifacts that degrade map quality.While offline methods can successfully clean these maps,they lack real-time capabilities.Conversely,online methods often suffer... Dynamic objects in LiDAR SLAM often introduce ghosting artifacts that degrade map quality.While offline methods can successfully clean these maps,they lack real-time capabilities.Conversely,online methods often suffer from state oscillation(where moving objects are misclassified as static when they temporarily stop)and incomplete point cloud removal.To address these challenges,we propose DGMSE,a real-time framework for removing dynamic point clouds in complex urban environments.Our approach consists of three sequential steps.First,the PointPillars 3D detection network quickly isolates potential dynamic objects,significantly reducing computational overhead.Second,to mitigate state oscillation,we introduce a five-state Markov chain to continuously estimate the probabilistic motion state of objects,which is not limited to simple moving-or-static classifications.This temporal memory prevents misclassification during sudden stops or motion transitions.Finally,instead of simply deleting all points inside a detection box,a density-adaptive clustering approach evaluates the geometric and motion characteristics of points,precisely segmenting dynamic objects from the static background without damaging permanent structures.Evaluated on the SemanticKITTI dataset,DGMSE achieves an F1-score of 0.95–0.99,achieving performance competitive with offline methods while satisfying real-time processing requirements. 展开更多
关键词 LiDAR SLAM dynamic object removal deep learning state estimation
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High-sensitivity phase estimation with a two-mode squeezed coherent state based on a Mach–Zehnder interferometer 认领 引用
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作者 Pengxiang Ruan Jun Liu +3 位作者 Chenlu Li Qingli Jing Mingming Zhang Dong-Xu Chen 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期389-400,共12页
A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily s... A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology. 展开更多
关键词 two-mode squeezed coherent state phase estimation quantum Cramér–Rao bound Heisenberg limit
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Hybrid Temporal Convolutional Network-Transformer Model Optimized by Particle Swarm Optimization for State of Charge Estimation of Lithium-Ion Batteries 认领 引用
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作者 Xincheng Han Hongyan Ma +1 位作者 Shuo Meng Chengzhi Ren 《Energy Engineering》 EI 2026年第4期505-530,共26页
Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implica... Lithium-ion(Li-ion)batteries stand as the dominant energy storage solution,despite their widespread adoption,precisely determining the state of charge(SOC)continues to pose significant difficulties,with direct implications for battery safety,operational reliability,and overall performance.Current SOC estimation techniques often demonstrate limited accuracy,particularly when confronted with complex operational scenarios and wide temperature variations,where their generalization capacity and dynamic adaptation prove insufficient.To address these shortcomings,this work presents a PSO-TCN-Transformer network model for SOC estimation.This research uses the Particle Swarm Optimization(PSO)method to automatically configure the architectural parameters of the Temporal Convolutional Network(TCN)and Transformer components.This automated optimization enhances the model’s ability to represent the dynamically evolving nature of SOC.Additionally,this integrated framework significantly increases the model’s capacity to capture SOC dynamics in complex operational scenarios.During training and evaluation using a comprehensive dataset that covers complex operating conditions and a broad temperature spanning from−20℃ to 40℃,the proposed model achieves a root mean square error(RMSE)of less than 0.6%,a maximum absolute error(MAXE)below 4.0%,and a coefficient of determination(R2)of 99.99%.Additional comparative experiments on data from an energy storage company further verify the model’s superior performance,with an RMSE of 1.18%and an MAXE of 1.95%.The implications of this work extend to the development of optimization strategies and hybrid architectures,providing insights that can be adapted for state estimation across a range of complex dynamic systems. 展开更多
关键词 Lithium-ion battery charge state estimation PSO algorithm PSO-TCN-transformer network
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Effect of ladle shroud immersion depth on unsteady three-phase flow in continuous casting tundish during ladle change-over process 认领 引用
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作者 Yu-Chao Yao Zhong-Qiu Liu +3 位作者 Yu-Ze Wei Ning Wang Jun Yang Bao-Kuan Li 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第1期443-457,共15页
The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbul... The transient phenomena of re-oxidation and slag entrapment occurring in the tundish during the ladle change-over process have been proven detrimental to clean steel production.Therefore,an unsteady three-phase turbulence model,coupling velocity,temperature,and phase field was established to study the effect of the ladle shroud immersion depth on the slag eye formation,slag entrainment,slag dragging,air dragging,and flow characteristics during the ladle change-over process of a two-strand tundish.The results showed that reducing the immersion depth decreases the high-velocity region area under the slag layer in the quasi-steady process.During the emptying stage,as the molten bath level gradually decreases,the outlet temperature exhibits a trend of initially decreasing and subsequently increasing across all three shroud immersion depths.However,under a 210 mm shroud immersion depth,molten slag and air are dragged into the shroud,forming slag droplets and causing significant fluctuations,with a maximum scalar velocity of 0.0764 m/s at the monitoring point.In the filling stage,air and molten slag are dragged into the molten bath,forming bubbles and slag droplets at an immersion depth of 210 mm.Bubbles are observed within the molten slag layer,which can readily cause an emulsification phenomenon,making it easier to be dragged as slag droplets.Additionally,the slag eye area measured under 210 mm immersion depth at 45 s is 0.303 m2,while the maximum scalar velocity of 2.4259 m/s is detected at 12 s.At an immersion depth of 360 mm,the average area of the slag eye is minimized to 0.06268 m2,with corresponding variances of 0.006753,representing the optimal immersion depth. 展开更多
关键词 Continuous casting tundish Ladle change-over Ladle shroud Immersion depth Three-phase flow Unsteady state
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Kalman filter based state estimation for the flexible multibody system described by ANCF 认领 引用 被引量：3
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作者 Zuqing Yu Shuaiyi Liu Qinglong Tian 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第5期207-218,共12页
The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with ... The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system. 展开更多
关键词 Nonlinear Kalman filter Absolute nodal coordinate formulation Flexible multibody system dynamics State estimation
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Enhanced robustness in constant modulus blind beamforming through L1-regularized state estimation with variable-splitting Kalman smoother and IEKS 认领 引用 被引量：1
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作者 Chuanhui HAO Bin ZHANG Xubao SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第6期573-590,共18页
This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel a... This paper aims to enhance the array Beamforming(BF) robustness by tackling issues related to BF weight state estimation encountered in Constant Modulus Blind Beamforming(CMBB). To achieve this, we introduce a novel approach that incorporates an L1-regularizer term in BF weight state estimation. We start by explaining the CMBB formation mechanism under conditions where there is a mismatch in the far-field signal model. Subsequently, we reformulate the BF weight state estimation challenge using a method known as variable-splitting, turning it into a noise minimization problem. This problem combines both linear and nonlinear quadratic terms with an L1-regularizer that promotes the sparsity. The optimization strategy is based on a variable-splitting method, implemented using the Alternating Direction Method of Multipliers(ADMM). Furthermore, a variable-splitting framework is developed to enhance BF weight state estimation, employing a Kalman Smoother(KS) optimization algorithm. The approach integrates the Rauch-TungStriebel smoother to perform posterior-smoothing state estimation by leveraging prior data. We provide proof of convergence for both linear and nonlinear CMBB state estimation technology using the variable-splitting KS and the iterated extended Kalman smoother. Simulations corroborate our theoretical analysis, showing that the proposed method achieves robust stability and effective convergence, even when faced with signal model mismatches. 展开更多
关键词 State estimation Constant modulus blind beamforming Kalman smoother Alternating direction method of multipliers Variable-splitting optimizer
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Distributed State and Fault Estimation for Cyber-Physical Systems Under DoS Attacks 认领 引用
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作者 Limei Liang Rong Su Haotian Xu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第1期261-263,共3页
Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded... Dear Editor,The letter deals with the distributed state and fault estimation of the whole physical layer for cyber-physical systems(CPSs) when the cyber layer suffers from DoS attacks. With the advancement of embedded computing, communication and related hardware technologies, CPSs have attracted extensive attention and have been widely used in power system, traffic network, refrigeration system and other fields. 展开更多
关键词 cyber physical systems refrigeration system traffic network dos attacks distributed state fault estimation embedded computing power system distributed state estimation
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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset 认领 引用
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作者 Zeyang Zhou Zachary James Ryan +5 位作者 Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad 《Computers, Materials & Continua》 SCIE EI 2025年第6期4795-4813,共19页
Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Dee... Accurate capacity and State of Charge(SOC)estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles.This study examines ten machine learning architectures,Including Deep Belief Network(DBN),Bidirectional Recurrent Neural Network(BiDirRNN),Gated Recurrent Unit(GRU),and others using the NASA B0005 dataset of 591,458 instances.Results indicate that DBN excels in capacity estimation,achieving orders-of-magnitude lower error values and explaining over 99.97%of the predicted variable’s variance.When computational efficiency is paramount,the Deep Neural Network(DNN)offers a strong alternative,delivering near-competitive accuracy with significantly reduced prediction times.The GRU achieves the best overall performance for SOC estimation,attaining an R2 of 0.9999,while the BiDirRNN provides a marginally lower error at a slightly higher computational speed.In contrast,Convolutional Neural Networks(CNN)and Radial Basis Function Networks(RBFN)exhibit relatively high error rates,making them less viable for real-world battery management.Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds,limiting the risk of overcharging or deep discharging.These findings highlight the trade-off between accuracy and computational overhead,offering valuable guidance for battery management system(BMS)designers seeking optimal performance under constrained resources.Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’robustness in diverse operating conditions. 展开更多
关键词 Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems
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Collaborative State Estimation for Coupled Transmission and Distribution Systems Based on Clustering Analysis and Equivalent Measurement Modeling 认领 引用
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作者 Hao Jiao Xinyu Liu +4 位作者 Chen Wu Chunlei Xu Zhijun Zhou Ye Chen Guoqiang Sun 《Energy Engineering》 EI 2025年第7期2977-2992,共16页
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig... With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy. 展开更多
关键词 Transmission and distribution collaboration cluster analysis parameter identification equivalent load state estimation
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DoS Attack Schedules for Remote State Estimation in CPSs With Two-hop Relay Networks Under Round-Robin Protocol 认领 引用
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作者 Shuo Zhang Lei Miao Xudong Zhao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1513-1515,共3页
Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve ef... Dear Editor,This letter investigates the optimal denial-of-service(DoS)attack scheduling targeting state estimation in cyber-Physical systems(CPSs)with the two-hop multi-channel network.CPSs are designed to achieve efficient,secure and adaptive operation by embedding intelligent and autonomous decision-making capabilities in the physical world.As a key component of the CPSs,the wireless network is vulnerable to various malicious attacks due to its openness[1].DoS attack is one of the most common attacks,characterized of simple execution and significant destructiveness[2].To mitigate the economic losses and environmental damage caused by DoS attacks,it is crucial to model and investigate data transmissions in CPSs. 展开更多
关键词 round robin protocol malicious attacks denial service attack two hop relay networks state estimation dos attack wireless network cyber physical systems
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Optimal Sensor Scheduling for Remote State Estimation With Partial Channel Observation 认领 引用
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作者 Bowen Sun Xianghui Cao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1510-1512,共3页
Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)fram... Dear Editor,This letter investigates the optimal transmission scheduling problem in remote state estimation systems over an unknown wireless channel.We propose a partially observable Markov decision Process(POMDP)framework to model the sensor scheduling problem.By truncating and simplifying the POMDP problem,we have established the properties of the optimal solution under the POMDP model,through a fixed-point contraction method,and have shown that the threshold structure of the POMDP solution is not easily attainable.Subsequently,we obtained a suboptimal solution via Qlearning.Numerical simulations are used to demonstrate the efficacy of the proposed Q-learning approach. 展开更多
关键词 truncating simplifying remote state estimation systems optimal transmission scheduling problem threshold structure sensor scheduling optimal solution partially observable markov decision process partially observable markov decision process pomdp framework
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Improved Zero-Dynamics Attack Scheduling With State Estimation 认领 引用 被引量：2
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作者 Zhe Wang Heng Zhang +1 位作者 Chaoqun Yang Xianghui Cao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第2期472-474,共3页
Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of a... Dear Editor,This letter focuses on how an attacker can design suitable improved zero-dynamics (ZD) attack signal based on state estimates of target system. Improved ZD attack is to change zero dynamic gain matrix of attack signal to a matrix with determinant greater than 1. 展开更多
关键词 change zero dynamic gain matrix target system state estimation scheduling attack signal improved zd state estimates improved zero dynamics attack
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Analysis and estimation of wave-induced Doppler shift from low-incidence-angle RAR based on sea state parameters 认领 引用
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作者 Jing Ye Yong Wan +3 位作者 Chenqing Fan Yongshou Dai Yisen Yang Xiangying Miao 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2025年第9期169-182,共14页
The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars ref... The research on ocean dynamics information plays a crucial role in understanding ocean phenomena, assessing marine environmental impacts, and guiding engineering designs. The Doppler information observed by radars reflects sea surface dynamics, to which ocean waves make important contributions. Low-incidence-angle real aperture radar(RAR)demonstrates great potential for independently observing vectorial Doppler information on the ocean surface. To systematically characterize and accurately estimate the wave-induced Doppler frequency shift(WVF) from lowincidence-angle RAR, this study conducts comprehensive influencing factor analysis and establishes sea-stateparameterized WVF models. First, a simulated WVF dataset is generated under a rotating low-incidence-angle RAR.The feature parameters of WVF are then determined by analysing contributing factors including wind waves, swells,and sea state parameters. Furthermore, two WVF models(WVF_Ku P9 with 9 inputs and WVF_Ku P4 with 4 inputs) are constructed by the Transformer encoder for different application scenarios. Both models achieve high accuracy for WVF estimation with root mean square errors(RMSE) of 1.874 Hz and 2.716 Hz, respectively. The reliability and superiority of the proposed models are validated through comparisons with the Ka DOP, which is a typical geophysical model function(GMF). The findings in this paper advance the understanding of WVF characteristics and generation mechanisms. The proposed estimation models can provide reliable estimates, offering critical references for lowincidence-angle RAR applications such as ocean surface current retrieval. 展开更多
关键词 wave-induced Doppler shift parameter estimation low-incidence-angle real aperture radar sea state parameters Transformer encoder
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Factor graph method for target state estimation in bearing-only sensor network 认领 引用
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作者 CHEN Zhan FANG Yangwang +1 位作者 ZHANG Ruitao FU Wenxing 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第2期380-396,共17页
For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.... For target tracking and localization in bearing-only sensor network,it is an essential and significant challenge to solve the problem of plug-and-play expansion while stably enhancing the accuracy of state estimation.This paper pro-poses a distributed state estimation method based on two-layer factor graph.Firstly,the measurement model of the bearing-only sensor network is constructed,and by investigating the observ-ability and the Cramer-Rao lower bound of the system model,the preconditions are analyzed.Subsequently,the location fac-tor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation.Building upon this foundation,the mechanism for propagating confidence mes-sages within the fusion factor graph is designed,and is extended to the entire sensor network to achieve global state estimation.Finally,groups of simulation experiments are con-ducted to compare and analyze the results,which verifies the rationality,effectiveness,and superiority of the proposed method. 展开更多
关键词 factor graph cubature information filtering bearing-only sensor network state estimation
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