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Traffic Control Based on Integrated kalman filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates kalman filtering and Q-learning.Unlike smoothing kalman filtering,our data fusion kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control systems.Remote state estimation(RSE)is an indispensable functional module of CPSs.Recently,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance degradation.This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against RSE.Firstly,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of RSE.Secondly,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from adversaries.Thirdly,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'perspectives.Finally,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks kalman filtering remote state estimation unreliable transmission channels

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Prediction of landslide block movement based on kalman filtering data assimilation method

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山地科学学报(英文) 2023年第9期20卷 2680-2691页

作者： LIU Yong XU Qing-jie LI Xing-rui YANG Ling-feng XU Hong Technology Innovation Center of Geohazards Automatic Monitoring Ministry of Natural Resources(Chongqing Institute of Geology and Mineral Resources)Chongqing 401120China School of Mechanical Engineering and Electronic Information China University of GeosciencesWuhan 430074China

Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.

关键词： Landslide block Movement state 6-degree of freedom pose kalman filtering Data assimilation Baishuihe landslide

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Power Quality Disturbance Identification Basing on Adaptive kalman Filter andMulti-Scale Channel Attention Fusion Convolutional Network

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Energy Engineering 2024年第7期121卷 1865-1882页

作者： Feng Zhao Guangdi Liu Xiaoqiang Chen Ying Wang School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

In light of the prevailing issue that the existing convolutional neural network(CNN)power quality disturbance identification method can only extract single-scale features,which leads to a lack of feature information and weak anti-noise performance,a new approach for identifying power quality disturbances based on an adaptive kalman filter(KF)and multi-scale channel attention(MS-CAM)fused convolutional neural network is suggested.Single and composite-disruption signals are generated through simulation.The adaptive maximum likelihood kalman filter is employed for noise reduction in the initial disturbance signal,and subsequent integration of multi-scale features into the conventional CNN architecture is conducted.The multi-scale features of the signal are captured by convolution kernels of different sizes so that the model can obtain diverse feature expressions.The attention mechanism(ATT)is introduced to adaptively allocate the extracted features,and the features are fused and selected to obtain the new main features.The Softmax classifier is employed for the classification of power quality disturbances.Finally,by comparing the recognition accuracy of the convolutional neural network(CNN),the model using the attention mechanism,the bidirectional long-term and short-term memory network(MS-Bi-LSTM),and the multi-scale convolutional neural network(MSCNN)with the attention mechanism with the proposed method.The simulation results demonstrate that the proposed method is higher than CNN,MS-Bi-LSTM,and MSCNN,and the overall recognition rate exceeds 99%,and the proposed method has significant classification accuracy and robust classification performance.This achievement provides a new perspective for further exploration in the field of power quality disturbance classification.

关键词： Power quality disturbance kalman filtering convolutional neural network attention mechanism

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kalman filtering for Delayed Singular Systems with Multiplicative Noise

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IEEE/CAA Journal of Automatica Sinica 2016年第1期3卷 51-58页

作者： Xiao Lu Linglong Wang Haixia Wang Xianghua Wang Key Laboratory for Robot and Intelligent Technology of Shandong Province Shandong University of Science and Technology College of Transportation Shandong University of Science and Technology

kalman filtering problem for singular systems is dealt with, where the measurements consist of instantaneous measurements and delayed ones, and the plant includes multiplicative noise. By utilizing standard singular value decomposition, the restricted equivalent delayed system is presented, and the kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma. The optimal kalman filter for the original system is given based on the above kalman filter by recursive Riccati equations, and a numerical example is presented to show the validity and efficiency of the proposed approach, where the comparison between the filter and predictor is also given.

关键词： kalman filtering filtering estimation reorganization of innovation analysis singular value decomposition

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Noise reduction of acoustic Doppler velocimeter data based on kalman filtering and autoregressive moving average models

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Acta Oceanologica Sinica 2020年第12期39卷 106-113页

作者： Chuanjiang Huang Fangli Qiao Hongyu Ma First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China Key Laboratory of Marine Sciences and Numerical Modeling Ministry of Natural ResourcesQingdao 266061China

Oceanic turbulence measurements made by an acoustic Doppler velocimeter(ADV)suffer from noise that potentially affects the estimates of turbulence statistics.This study examines the abilities of kalman filtering and autoregressive moving average models to eliminate noise in ADV velocity datasets of laboratory experiments and offshore observations.Results show that the two methods have similar performance in ADV de-noising,and both effectively reduce noise in ADV velocities,even in cases of high noise.They eliminate the noise floor at high frequencies of the velocity spectra,leading to a longer range that effectively fits the Kolmogorov-5/3 slope at midrange frequencies.After de-noising adopting the two methods,the values of the mean velocity are almost unchanged,while the root-mean-square horizontal velocities and thus turbulent kinetic energy decrease appreciably in these experiments.The Reynolds stress is also affected by high noise levels,and de-noising thus reduces uncertainties in estimating the Reynolds stress.

关键词： noise kalman filtering autoregressive moving average model turbulence acoustic Doppler velocimeter

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Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and kalman filtering

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控制理论与应用（英文版） 2013年第1期11卷 27-34页

作者： Xingbo WANG Huanshui ZHANG Minyue FU School of Control Science and Engineering Shandong University School of Electrical Engineering and Computer Science University of Newcastle

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.

关键词： Target tracking Wireless sensor network Maximum likelihood estimation kalman filtering Fisher information matrix Sensor selection

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An On-Line Modeling Based kalman filtering Process for Time-Interval-Variable Sequences with Application to Astronomic Surveying

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Journal of Systems Engineering and Electronics 2003年第2期14卷 52-56页

作者：韩建国孙才红李彦琴 School of Information Science and Technology Beijing University of Chemical Technology Beijing 100029 P. R. China

The problem of variable sampling time interval which appears in application of kalman filtering is analyzed and the corresponding filtering process with or without present transition matrix is suggested, then an application experiment for astronomical surveying is introduced. In this process, the known stochastically variable sampling time intervals play the roles as deterministic input sequences of the state-space description, and the corresponding matrix and (if needed) state transition matrix can be established by performing real-time and structure-linear system identification.

关键词： kalman filtering Variable sampling time interval Real-time and structure-linear system identification.

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Suboptimal distributed kalman filtering fusion with feedback

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Journal of Systems Engineering and Electronics 2005年第4期16卷 746-749页

作者： Zhao Minhua Zhu Zhuanmin Shi Meng Peng Qinke Huang Yongxuan School of Economics & Finance Xi'an Jiaotong Univ. Xi'an 710049 P. R. China School of Sdenee Xi'an Jiaotong Univ. Xi'an 710049 P. R. China School of Electronics and information Engineering Xi'an Jiaotong Univ. Xi'an 710049 P. R. China

In order to improve the accuracy of fusion algorithm, feedback is introduced into kalman filtering fusion. Fusion center broadcasts its latest estimated states to the local sensors, which can improve the performance of local tracking error through reducing the oovariance of each local error, and only needs calculating the trace of error variance matrices without calculating the inverse of error variance matrices. Simulation results show that it can reduce the ecmputational complexity and the oovariance of error, and it is oonvenient for engineering applications.

关键词： feedback kalman filtering data fusion.

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Data fusion for target tracking in wireless sensor networks using quantized innovations and kalman filtering

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Science China(Information Sciences) 2012年第3期55卷 530-544页

作者： XU Jian1,2 , LI JianXun1,2 & XU Sheng3 1Department of Automation, Science and Technology on Avionics Integration Laboratory, Shanghai Jiao Tong University, Shanghai 200240, China 2Department of Automation, System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China 3School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Department of Automation Science and Technology on Avionics Integration Laboratory Shanghai Jiao Tong University Shanghai China Department of Automation System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

A novel networked data-fusion method is developed for the target tracking in wireless sensor networks (WSNs). Specifically, this paper investigates data fusion scheme under the communication constraint between the fusion center and each sensor. Such a message constraint is motivated by the bandwidth limitation of the communication links, fusion center, and by the limited power budget of local sensors. In the proposed scheme, each sensor collects one noise-corrupted sample, performs a quantizing operation, and transmits quantized message to the fusion center. Then the fusion center combines the received quantized messages to produce a final estimate. The novel data-fusion method is based on the quantized measurement innovations and decentralized kalman filtering (DKF) with feedback. For the proposed algorithm, the performance analysis of the estimation precision is provided. Finally, Monte Carlo simulations show the effectiveness of the proposed scheme.

关键词： data fusion target tracking limited bandwidth quantized innovation kalman filtering

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