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.

Unknown Environment Measurement Mapping by Unmanned Aerial Vehicle Using Kalman Filter-Based Low-Cost Estimated Parallel 8-Beam LIDAR

The measurement and mapping of objects in the outer environment have traditionally been conducted using ground-based monitoring systems,as well as satellites.More recently,unmanned aerial vehicles have also been employed for this purpose.The accurate detection and mapping of a target such as buildings,trees,and terrains are of utmost importance in various applications of unmanned aerial vehicles(UAVs),including search and rescue operations,object transportation,object detection,inspection tasks,and mapping activities.However,the rapid measurement and mapping of the object are not currently achievable due to factors such as the object’s size,the intricate nature of the sites,and the complexity of mapping algorithms.The present system introduces a costeffective solution for measurement and mapping by utilizing a small unmanned aerial vehicle(UAV)equipped with an 8-beam Light Detection and Ranging(LiDAR)system.This approach offers advantages over traditional methods that rely on expensive cameras and complex algorithm-based approaches.The reflective properties of laser beams have also been investigated.The system provides prompt results in comparison to traditional camerabased surveillance,with minimal latency and the need for complex algorithms.The Kalman estimation method demonstrates improved performance in the presence of noise.The measurement and mapping of external objects have been successfully conducted at varying distances,utilizing different resolutions.

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.

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.

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.

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.

一种基于模型概率单调性变化的自适应IMM-UKF改进算法

针对现有交互式多模型(IMM)算法模型间切换迟滞和转换速率慢的缺点,提出一种基于模型概率单调性变化的自适应交互式多模型无迹卡尔曼滤波改进算法(mIMM-UKF)。该算法利用后验信息模型概率的单调性,对马尔可夫转移概率矩阵及模型估计概率进行二次修正,加快了匹配模型的切换速度及转换速率。仿真结果表明,与现有算法相比,该算法通过快速切换匹配模型,有效提高了水下目标跟踪精度。

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EM-KF算法的微地震信号去噪方法

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。

基于IAAKF算法的结构激励识别与响应重构

针对传统卡尔曼滤波(Kalman filter,KF)算法实际应用于响应重构时,需要已知结构外部激励并预设先验恒定噪声方差的问题,提出了一种基于IAAKF(innovation-based adaptive augmented Kalman filter)算法的结构激励识别与响应重构方法。首先,基于增广状态空间模型将外部激励向量与状态向量联合构成增广状态向量,并根据增广新息统计特性实时自适应地调整卡尔曼滤波增益和状态估计误差协方差矩阵;其次,仅借助加速度传感器根据模态法来识别锤击激励,并且重构出加速度、速度、位移以及应变响应等数据;最后,对起重机桁架和简支梁分别进行数值模拟和试验分析。结果表明,所提方法能够有效地自适应调整噪声方差和识别结构外部激励,从而实现结构响应重构。

CNN-LSTM车辆运动状态识别的AUKF组合导航方法

针对固定的噪声协方差难以适应车辆不同运动行为下噪声统计特性差异大的问题,提出了一种基于卷积神经网络与长短期记忆网络(CNN-LSTM)的车辆运动状态识别自适应无迹卡尔曼滤波(AUKF)组合导航方法。首先,应用CNN-LSTM网络模型进行车辆运动状态识别,解决车辆自我运动不确定性的问题;其次,将特定运动状态约束下的噪声协方差应用于UKF的时间更新与量测更新;最后,将所提方法在采集的数据集上进行验证。实验结果表明,与经典的UKF算法相比,所提方法的位置均方根误差与速度均方根误差分别下降了22.67%与2.63%,验证了所提方法的有效性。

基于分数阶模型多新息UKF动力电池SOC估算研究

动力电池管理技术是保障新能源汽车高效、安全和可靠运行的核心和关键。动力电池的荷电状态(SOC)是动力电池管理技术的基础,然而动力电池SOC的不确定影响因素太多,如何精确估算动力电池的SOC成为关键问题。针对SOC难以精确获得的问题,搭建了动力电池测试平台,开展了动力电池的常规性能测试、寿命测试,建立了基于分数阶理论的动力电池分数阶模型,将多新息理论与分数阶模型无迹卡尔曼滤波算法结合,提出了分数阶模型多新息无迹卡尔曼滤波(FOMIUKF)算法,并采用该算法对动力电池进行SOC估算。在不同的环境温度、动态工况、SOC初始值条件下对基于不同算法的动力电池SOC估算精度进行了对比分析。结果表明:基于FOMIUKF算法对动力电池SOC估算结果的平均绝对误差和均方根误差的值最小。在不同的动态工况下,采用FOMIUKF算法对动力电池SOC估算结果的平均绝对误差的最大值约为1.04%,对SOC估算结果的均方根误差最大值约为0.8586%,这表明采用FOMIUKF算法对动力电池SOC估算结果的精度高于EKF、UKF、FOUKF算法。

基于DKF-Bi-LSTM的阀控式铅酸电池SOC在线估计方法

精准估计阀控式铅酸蓄电池的荷电状态(SOC)对变电站直流系统的可靠性和安全性有着重要的作用,为提高SOC估算精度,提出一种基于DKF-Bi-LSTM的铅酸蓄电池SOC在线估计方法,基于二级结构的双卡尔曼滤波算法,分别进行模型估计和状态估计。通过卡尔曼滤波算法对模型参数进行动态跟踪,进而基于扩展卡尔曼滤波算法在线估算电池SOC值。将在线估算结果、电流、电压、温度值作为Bi-LSTM神经网络的输入,电池SOC预测值作为网络输出,实现对电池SOC的在线估计。经测试发现,与DKF和Bi-LSTM算法相比,DKF-Bi-LSTM算法的SOC预测均方根误差更小,其SOC在线估计方法具有更高的准确性。

基于改进ESKF的植保无人机时延位姿补偿算法

为解决全球导航卫星系统和惯性测量单元融合时间不同步问题,提高植保无人机位姿估计精度,本文根据植保无人机大惯性、强振动的特性提出一种基于改进误差状态卡尔曼的时延位姿补偿算法。首先对名义状态变量线性预测,引入渐消因子提高强振动环境下的系统稳定性;接着采用互补滤波对角速度补偿,对姿态误差状态变量修正;最后结合测量的延迟时间,使用互补滤波外推数据,提高大惯性特性下的速度位置精度。实验结果表明,相较于误差状态卡尔曼算法,横滚角和俯仰角均方根误差减少0.2669°和0.2414°,偏航角均方根误差减少0.0764°;正常航迹植保作业下,东北天方向速度均方根误差减少0.2105、0.1849、0.2388 m/s;东北天方向位置均方根误差分别减少0.21、0.19、0.23 m,有效提高位姿估计精度。

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。

基于改进SHKF算法的UWB/IMU组合定位方法

针对复杂环境下超宽带(UWB)无线定位系统存在非视距(NLOS)及随机误差的问题,提出一种基于改进Sage-Husa卡尔曼滤波算法(SHKF)的UWB/IMU组合定位方法。首先,设计了一种基于概率密度的提升树,将UWB/IMU特征数据的概率分布密度引入提升树的损失函数中,鉴别出NLOS信号;然后,设计了一种改进SHKF算法,根据新息变化趋势定义自适应因子,实时调整对新息误差修正的策略以调节历史噪声对当前定位的影响,进而提升UWB/IMU组合定位的稳定性和精度。实验结果表明,所提方法将NLOS信号鉴别精度提升至99.12%,定位均方根误差降低至4.30 cm,提升了复杂环境下UWB/IMU组合系统定位精度。

基于EKF和UKF的随钻姿态解算方法研究

目的为解决煤层松软中随钻测量系统测量精度不高的问题。方法提出一种改进的无迹卡尔曼滤波(UKF)和扩展卡尔曼滤波(EKF),分别应用于钻具的姿态滤波算法中并作比较。该方法基于旋转坐标变换的四元数理论和陀螺测量原理,建立钻具姿态传感器数据的非线性观测方程和状态方程,以四元数将测量数据进行转换与更迭,最终消除惯性传感器数据中的误差。与EKF算法相比较,UKF算法利用了UT变换对非线性函数的概率密度分布进行近似,没有忽略高项阶,因此对于非线性分布的统计量有较好的计算精度。结果经仿真验证,UKF的各个滤波误差峰峰值以及标准差小于EKF。结论改进的UKF的滤波算法精度明显高于EKF滤波算法,更加有效地去除惯性传感器中的干扰噪声,有利于提高微机电系统(MEMS)惯性传感器的测量精度,进而提高钻进效率。

基于MACF-CKF多传感器融合的姿态解算算法

针对惯性导航测量单元姿态解算精度低的问题,提出了一种基于多传感器隶属度自适应互补滤波(Membership Adaptive Complementary Filtering,MACF)和容积卡尔曼滤波(Cubature Kalman Filter,CKF)相融合的姿态解算算法。使用指数加权移动平均修正陀螺仪噪声偏差,为了避免出现陀螺仪和加速度计的权重在互补滤波中分配不当而导致俯仰角和横滚角误差较大的现象,通过构造陀螺仪偏差的隶属度函数,判断互补滤波对陀螺仪的信任度,根据信任度动态调整互补滤波自适应因子,同时用磁力计和陀螺仪进行CKF来解决航向角发散的问题。实验表明:所提出的算法无论在静态条件还是动态条件下均能快速、准确实现姿态解算,在动态车载实验中,横滚角和俯仰角精度分别提升了24.5%和63.2%,航向角提升了48.8%,可以保证解算精度。