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 dual adaptive unscented Kalman filter algorithm for SINS-based integrated navigation system

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

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.

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

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.

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.

Application of Kalman Filter Method in the Forecast of Temperature in Nanchang

A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.

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.

Exploring on Hierarchical Kalman Filtering Fusion Accuracy

Aim To analyze the traditional hierarchical Kalman filtering fusion algorithm theoretically and point out that the traditional Kalman filtering fusion algorithm is complex and can not improve the tracking precision well, even it is impractical, and to propose the weighting average fusion algorithm. Methods The theoretical analysis and Monte Carlo simulation methods were ed to compare the traditional fusion algorithm with the new one,and the comparison of the root mean square error statistics values of the two algorithms was made. Results The hierarchical fusion algorithm is not better than the weighting average fusion and feedback weighting average algorithm The weighting filtering fusion algorithm is simple in principle, less in data, faster in processing and better in tolerance.Conclusion The weighting hierarchical fusion algorithm is suitable for the defective sensors.The feedback of the fusion result to the single sersor can enhance the single sensorr's precision. especially once one sensor has great deviation and low accuracy or has some deviation of sample period and is asynchronous to other sensors.

Kalman滤波在自动化监测数据噪声处理上的应用研究

对Kalman滤波在静力水准和固定式测斜仪原始数据降噪处理中的应用进行研究。结果表明,在缓慢变形条件下,Kalman滤波可以有效过滤原始数据中的噪声,提供试验结果,还原监测对象真实变形情况。但在突发变形情况下,Kal-man滤波反应滞后。因此,实际应用中应综合使用滤波前和滤波后的数据,以更准确地研判变形趋势和规律,为自动监测数据的噪声处理提供了新的思路和方法。通过Kalman滤波的应用,可以进一步提高监测数据的准确性和可靠性,为工程安全监测和地质灾害预警等领域提供支持。

基于集合Kalman滤波的中长期径流预报

为降低中长期径流预报的不确定性,增加水电站水库的发电效益,针对现有方法侧重于提高单一预报模型确定性预报结果的准确性以降低径流预报不确定性的问题,提出一种基于集合Kalman滤波的入库径流确定性预报方法。以旬为预见期的锦西水库实例验证结果表明:相比传统的单一预报模型和传统的信息融合预报模型,基于集合Kalman滤波的中长期径流预报可使RMSE降低4.78 m^(3)/s,合格率可提高0.56%,且更有效地降低了汛期预报的不确定性,得到了更加准确、可靠的确定性径流预报结果,可为开展流域梯级水电站优化调度提供技术支持。

基于参数估计和Kalman滤波的单通道盲源分离算法

针对存在频谱混叠通信信号的单通道盲源分离(single channel blind source separation,SCBSS)问题,提出一种基于参数估计和Kalman滤波的SCBSS算法。首先,针对根多重信号分类(root multiple signal classification,Root-MUSIC)算法在相近载频估计方面的局限性,提出一种自适应的Root-MUSIC算法,对接收到的盲混合信号的源信号数目和载频进行估计;其次,将Kalman滤波的思想引入到SCBSS算法中,根据估计得到的源信号参数构造信号模型,将其作为Kalman滤波系统的观测向量,执行“时间更新”和“测量更新”两个过程,得到源信号的最佳估计,实现单通道盲源分离。仿真结果表明,所提算法能够有效地从存在频谱混叠的单路接收信号中准确地分离出多路源信号,比传统的算法分离精度高,运算速度快。

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。

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

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.

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。

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.

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。

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.