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

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.

WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains.However,in real-world scenarios,accurate predictions are challenging due to various interferences.This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments.By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the catenary.To improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and KF.In this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy rules.The inputs of the model include time,temperature,and historical displacement,while the output is the predicted displacement.Furthermore,the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance.These modifications contribute to more accurate predictions.Lastly,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method.The test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.

Unscented Kalman filter for a low-cost GNSS/IMU-based mobile mapping application under demanding conditions

For the last two decades,low-cost Global Navigation Satellite System(GNSS)receivers have been used in various applications.These receivers are mini-size,less expensive than geodetic-grade receivers,and in high demand.Irrespective of these outstanding features,low-cost GNSS receivers are potentially poorer hardwares with internal signal processing,resulting in lower quality.They typically come with low-cost GNSS antenna that has lower performance than their counterparts,particularly for multipath mitigation.Therefore,this research evaluated the low-cost GNSS device performance using a high-rate kinematic survey.For this purpose,these receivers were assembled with an Inertial Measurement Unit(IMU)sensor,which actively transmited data on acceleration and orientation rate during the observation.The position and navigation parameter data were obtained from the IMU readings,even without GNSS signals via the U-blox F9R GNSS/IMU device mounted on a vehicle.This research was conducted in an area with demanding conditions,such as an open sky area,an urban environment,and a shopping mall basement,to examine the device’s performance.The data were processed by two approaches:the Single Point Positioning-IMU(SPP/IMU)and the Differential GNSS-IMU(DGNSS/IMU).The Unscented Kalman Filter(UKF)was selected as a filtering algorithm due to its excellent performance in handling nonlinear system models.The result showed that integrating GNSS/IMU in SPP processing mode could increase the accuracy in eastward and northward components up to 68.28%and 66.64%.Integration of DGNSS/IMU increased the accuracy in eastward and northward components to 93.02%and 93.03%compared to the positioning of standalone GNSS.In addition,the positioning accuracy can be improved by reducing the IMU noise using low-pass and high-pass filters.This application could still not gain the expected position accuracy under signal outage conditions.

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

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.

Kalman-Filtering红外焦平面非均匀性仿真研究

非均匀性红外图像的仿真技术,在非均匀性校正技术的研究中起着十分重要的作用。针对红外探测器的响应参数符合高斯—马尔可夫(Gauss-Markov)过程,引入一个线性响应模型,建立了状态方程和观测方程,进而在一定的初始条件下,使用卡尔曼滤波(Kalman-Filtering)的方法,完成了红外图像的仿真计算。仿真的红外图像经过理论分析,效果很理想。

Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter

Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.

Improved Adaptive Iterated Extended Kalman Filter for GNSS/INS/UWB-Integrated Fixed-Point Positioning

To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.

Robust vision-based displacement measurement and acceleration estimation using RANSAC and Kalman filter

Computer vision(CV)-based techniques have been widely used in the field of structural health monitoring(SHM)owing to ease of installation and cost-effectiveness for displacement measurement.This paper introduces computer vision based method for robust displacement measurement under occlusion by incorporating random sample consensus(RANSAC).The proposed method uses the Kanade-Lucas-Tomasi(KLT)tracker to extract feature points for tracking,and these feature points are filtered through RANSAC to remove points that are noisy or occluded.With the filtered feature points,the proposed method incorporates Kalman filter to estimate acceleration from velocity and displacement extracted by the KLT.For validation,numerical simulation and experimental validation are conducted.In the simulation,performance of the proposed RANSAC filtering was validated to extract correct displacement out of group of displacements that includes dummy displacement with noise or bias.In the experiment,both RANSAC filtering and acceleration measurement were validated by partially occluding the target for tracking attached on the structure.The results demonstrated that the proposed method successfully measures displacement and estimates acceleration as compared to a reference displacement sensor and accelerometer,even under occluded conditions.

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

It is necessary to know the status of adhesion conditions between wheel and rail for efficient accelerating and decelerating of railroad vehicle.The proper estimation of adhesion conditions and their real-time implementation is considered a challenge for scholars.In this paper,the development of simulation model of extended Kalman filter(EKF)in MATLAB/Simulink is presented to estimate various railway wheelset parameters in different contact conditions of track.Due to concurrent in nature,the Xilinx®System-on-Chip Zynq Field Programmable Gate Array(FPGA)device is chosen to check the onboard estimation ofwheel-rail interaction parameters by using the National Instruments(NI)myRIO®development board.The NImyRIO®development board is flexible to deal with nonlinearities,uncertain changes,and fastchanging dynamics in real-time occurring in wheel-rail contact conditions during vehicle operation.The simulated dataset of the railway nonlinear wheelsetmodel is tested on FPGA-based EKF with different track conditions and with accelerating and decelerating operations of the vehicle.The proposed model-based estimation of railway wheelset parameters is synthesized on FPGA and its simulation is carried out for functional verification on FPGA.The obtained simulation results are aligned with the simulation results obtained through MATLAB.To the best of our knowledge,this is the first time study that presents the implementation of a model-based estimation of railway wheelset parameters on FPGA and its functional verification.The functional behavior of the FPGA-based estimator shows that these results are the addition of current knowledge in the field of the railway.

Nonlinear Bayesian Estimation： From Kalman Filtering to a Broader Horizon

This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date,one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective,which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics(e.g., mean and covariance) conditioned on a system's measurement data.This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering(KF)techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter/input estimation.

Phase noise filtering and phase unwrapping method based on unscented Kalman filter

This paper presents a new phase unwrapping algorithm based on the unscented Kalman filter（UKF） for synthetic aperture radar（SAR） interferometry.This method is the result of combining an UKF with path-following strategy and an omni-directional local phase slope estimator.This technique performs simultaneously noise filtering and phase unwrapping along the high-quality region to the low-quality region,which is also able to avoid going directly through the noisy regions.In addition,phase slope is estimated directly from the sample frequency spectrum of the complex interferogram,by which the underestimation of phase slope is overcome.Simulation and real data processing results validate the effectiveness of the proposed method,and show a significant improvement with respect to the extended Kalman filtering（EKF） algorithm and some conventional phase unwrapping algorithms in some situations.

Research on Kalman Filtering Algorithmfor Deformation Information Series ofSimilar Single-Difference Model

Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.

基于温度场与D-Kalman参数估计的光学电压传感温度补偿方法

光学电压传感器在温度稳定性方面仍有亟待解决的问题,一是电光晶体在温度变化时存在温度梯度,导致表面温度与光路温度不等;二是晶体物性参数也会受到温度影响。为此提出一种基于温度场与双卡尔曼滤波(Dual Kalman, D-Kalman)参数估计的温度补偿方法。以锗酸铋晶体为研究对象,在对传感器输出信号进行交直流分离的基础上,先利用半解析法建立晶体暂态温度场模型,再分别通过卡尔曼滤波与中心差分卡尔曼滤波实现对晶体内部温度和初始温度下晶体折射率的状态估计,最后将修正参数与传感器输出信号高频分量相结合计算补偿电压。实验结果表明,传感器在外界温度为[20℃,40℃]以0.5℃/min速率不断升高的环境下,暂态温度场解析式的仿真精度在0.02%以内,实验测量精度在0.2%左右,补偿输出电压测量精度优于0.52%。与同平台下反向传播神经网络温度补偿效果以及不同平台下的补偿效果相比,该方法提高了传感器测量精度。

Notes on Convergence and Modeling for the Extended Kalman Filter

The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems.A critical analysis of both the Kalman filter(KF)and the extended Kalman filter(EKF)will be provided,along with examples to illustrate some important issues related to filtering convergence due to system modeling.A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the algorithms.Adding fictitious process noise to the system model assumed by the filter designers for convergence assurance is being investigated.A comparison of estimation accuracy with linear and nonlinear measurements is made.Parameter identification by the state estimation method through the augmentation of the state vector is also discussed.The intended readers of this article may include researchers,working engineers,or engineering students.This article can serve as a better understanding of the topic as well as a further connection to probability,stochastic process,and system theory.The lesson learned enables the readers to interpret the theory and algorithms appropriately and precisely implement the computer codes that nicely match the estimation algorithms related to the mathematical equations.This is especially helpful for those readers with less experience or background in optimal estimation theory,as it provides a solid foundation for further study on the theory and applications of the topic.

基于Kalman滤波与应变信号的舰船轴系推力辨识研究

在线、实时、准确监测舰船螺旋桨推力对船-机-桨匹配设计、舰船快速性预报及推进轴系健康管理等具有重要意义。然而,受轴系振动及环境干扰等测量噪声影响,螺旋桨推力产生的微弱应变信号易被测量噪声淹没,导致难以准确测量推力。当前,一些常用的信号降噪方法,比如傅里叶变换、小波分析等均是基于纯数据降噪,未考虑测量数据中潜藏的力学机制。不同于这类降噪方法,Kalman滤波可同时考虑测量数据噪声及数据中的力学机制,对目标实现最小方差无偏估计,因而有更高的估计精度。因此,本文利用Kalman滤波结合应变测量信号提出一种螺旋桨推力高精度、在线辨识方法。以恒定转速、变转速及低频波动转速3种工况为例,研究了不同信噪比下本文方法的推力辨识精度与鲁棒性。研究表明,在信噪比仅为20 d B时,推力辨识最大相对误差仅为4.85%,因此本文方法在低信噪比下仍有很高的辨识精度与鲁棒性。同时,本文提出方法属于时域辨识方法,在转速突变、螺旋桨缠绕渔网等突发工况时亦能实时跟踪推力变化,因此可用于螺旋桨推力及轴系状态的在线、实时监测。

Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

Kalman Filter-Based CNN-BiLSTM-ATT Model for Traffic Flow Prediction

To accurately predict traffic flow on the highways,this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism(CNN-BiLSTM-Attention)traffic flow prediction model based on Kalman-filtered data processing.Firstly,the original fluctuating data is processed by Kalman filtering,which can reduce the instability of short-term traffic flow prediction due to unexpected accidents.Then the local spatial features of the traffic data during different periods are extracted,dimensionality is reduced through a one-dimensional CNN,and the BiLSTM network is used to analyze the time series information.Finally,the Attention Mechanism assigns feature weights and performs Soft-max regression.The experimental results show that the data processed by Kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention model.Compared with the CNN-BiLSTM model,the Root Mean Square Error(RMSE)of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error(MAE)by 12.38,and the accuracy of the improved model is almost free from non-working days.To further verify the model’s applicability,the experiments were re-run using two other sets of fluctuating data,and the experimental results again demonstrated the stability of the model.Therefore,the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.

Cubature Kalman Filter Based Millimeter Wave Beam Tracking for OTFS Systems

In this paper,a Millimeter wave(mmWave)beam tracking problem is studied in orthogonal time frequency space(OTFS)systems.Considering the nonlinearity of beamforming and the constraints of existing Kalman-filtering based beam tracking schemes,we propose a novel Cubature Kalman Filter(CKF)framework tracking the channel state information(CSI)to manage the challenge of highspeed channel variation in single-user moving scene for OTFS systems.Aiming for low complexity for mobile settings,this paper trains only one beam pair to track a path to maintain the reliable communication link in the analog beamforming architecture.Simulation results show that our proposed method has better tracking performance to improve the accuracy of the estimated beam angle compared with prior work.