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。

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。

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.

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。

基于CamShift与Kalman相结合的目标跟踪算法研究

目标跟踪是机器视觉领域的一项重要技术。传统的CamShift目标跟踪算法具有时间复杂度低、运算速度快的优点,在简单背景下具有良好的跟踪效果。但当跟踪目标处于部分遮挡的复杂情况下时,容易出现目标丢失的情况,从而影响后续的跟踪。Kalman滤波算法在目标跟踪任务中,能够有效地预测跟踪目标下一时刻可能出现的位置,且算法简单方便。现将CamShift算法与Kalman滤波算法相结合来优化传统的CamShift算法,实验结果表明:优化后的算法不但保留了传统CamShift算法的优点,而且在一定程度上可以预测跟踪目标的行动轨迹,更好地实现目标跟踪任务,解决了传统CamShift算法在目标被部分遮挡的情况下容易出现的目标丢失的情况;同时,运算结果准确,基本没有预测误差。

基于自适应强跟踪Kalman滤波的GNSS跟踪环路设计

为提高GNSS接收机跟踪环路在复杂环境下的跟踪性能,提出一种基于自适应强跟踪Kalman滤波(ASTKF)的跟踪环路,在传统跟踪环路的基础上,以鉴相器输出为观测量进行自适应强跟踪Kalman滤波,滤波结果用于计算导航滤波器的观测量,同时将伪码频率和载波多普勒频率反馈到码NCO和载波NCO,在ASTKF中使用基于卡方分布的渐消因子计算方法,提升跟踪环路鲁棒性。半物理仿真实验表明,相比于基于Kalman滤波的跟踪环路和基于强跟踪Kalman滤波(STKF)的跟踪环路,所提出方法在水平方向上的位置误差和速度误差减小20%以上,有效提高了卫星导航接收机的定位性能。

改进扩展Kalman滤波的显微视觉压电驱动定位

在显微视觉领域,压电驱动定位技术因其在微观尺度的高精度特性和灵活性备受关注。然而,由于定位过程中涉及图像处理、传输和控制等方面的时延,导致图像雅可比矩阵的估计会出现较大误差。因此,本文提出了一种改进扩展Kalman滤波算法用来预测图像雅可比矩阵,大幅度降低时间延迟因素。首先,将辨识得到的Bouc-Wen模型与扩展Kalman滤波算法的状态观测方程相结合,使得状态观测方程更全面地考虑压电平台的迟滞非线性特性,有效地提高了对压电平台速度和位置的预测;其次,结合Bouc-Wen模型的扩展Kalman滤波算法在面对非线性问题时,采用的是泰勒级数,这将导致扩展Kalman滤波算法对高度非线性的函数无法提供良好的近似,从而导致在估计雅可比矩阵的时候引入较大的近似误差,故本文将采用神经网络对高度非线性函数进行近似,进而对图像雅可比矩阵进行估计。最后,通过搭建一个显微视觉的压电驱动实验平台,进行位置跟踪实验,仿真实验表明,输入信号分别为正弦信号和三角波信号时,改进扩展Kalman滤波算法跟踪误差均值分别为0.199μm和0.132μm,而扩展Kalman滤波算法的跟踪误差均值分别为0.692μm和0.513μm,结果验证了改进算法的优越性和可行性。

带乘性噪声的欠观测系统无迹增量Kalman融合估计

研究了带乘性噪声的非线性欠观测系统的多传感融合估计问题。采用虚拟状态向量与虚拟噪声,并为虚拟状态设计一步预报器与状态更新方程。针对非线性欠观测系统提出了无迹增量Kalman滤波算法(UIKF)。提出了对角矩阵加权的融合增量卡尔曼滤波器。通过对比分析,得到增量估计值精度要高于标准估值精度,加权融合曲线的估值精度要高于单一子传感器估值精度,验证了在滤波过程中使用增量滤波方法对状态估计的优化。

基于集合变换Kalman滤波的流场高效重构

湍流场的准确估计在航空航天领域具有重要意义,现有的获取手段在分辨率或者准确性方面是不足的。实验测量准确却往往测点数量有限,数值计算能获得全场数据,但精度却难以保障。数据同化方法融合了实验观测和数值模拟,是进行流场重构的有效工具。探索了基于集合变换Kalman滤波(ensemble transform Kalman filter,ETKF)的数据同化方法在空间流场重构方面的有效性,并讨论了不同迭代更新模式的重构精度和计算效率,即状态变量基于湍流模型更新的ETKF-M和基于流场数据更新的ETKF-D。以ONERA M6机翼作为数值算例,结合风洞实验翼型表面271测压孔的压力测量数据进行算法实验,结果表明ETKF方法的不同迭代模式均有效修正了湍流模型的预测,并且ETKF-D相对于ETKF-M提升了83%的计算效率。此外,选取两组不同位置的1/4实验测点进行同化实验,得到不同精度的结果,这表明重构的精度与同化测点的位置和数量密切相关。

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。

基于离散Kalman滤波的计算机网络通信异常节点识别方法

传统异常节点识别方法存在效率较低、精准度较差的局限性,为解决以上问题,现提出基于离散Kalman滤波的计算机网络通信异常节点识别方法。首先,基于离散Kalman滤波构建通信节点测量信道模型,使系统的平方误差降至最小,从而为离散过程的优化设计提供递推算法,其次,提取计算机网络通信异常节点特征,将所有的异常进攻事件都分割成不同的数据集合,最后,实现计算机网络通信异常节点的定位并识别。实验结果证明:基于离散Kalman滤波的计算机网络通信异常节点识别方法识别准确率可达到99%以上,能够准确识别出异常节点,具有较高的识别精度。

模糊自适应Kalman滤波算法在SINS/DR组合导航的应用

针对里程仪测量误差导致组合导航精度降低的问题,提出基于系统工作状态和滤波器新息状态相结合的模糊自适应算法。根据新息的变化确定模糊规则,修正里程仪输出增益,使新息始终保持在零均值附近,利用修正后的新息修正观测噪声方差,降低导航定位的偏差。仿真实验结果证明,该算法能够很好地提高组合导航定位的精度。

基于Kalman滤波思想的接触线高度检测数据粗差修正算法

接触线高度平顺性是评价接触网设备动态运行质量的重要指标。针对接触线高度检测数据中的粗差,基于Kalman滤波思想提出相应的粗差辨识与修正算法。将接触线高度视作一个连续平稳系统,利用矩形窗函数定义系统状态值,实现检测数据的分段线性化;依据系统状态值的回归预测方法和状态差异的量化方案,构建粗差辨识与修正算法,并引入适用于锚段关节处双支接触线采集特征的回溯机制;基于大量实测数据,采用显著性检验等统计方法确定算法参数,并从回溯、修正、运用和实时性等方面,对算法有效性和可行性进行分析验证,结果表明:该算法能够完成各类接触线高度检测粗差的高速实时辨识与修正。

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.

机械臂外力的时延强跟踪Kalman估计与自学习控制

为了提高机械臂与环境交互过程中的接触力估计精度和关节控制精度,提出了基于时延强跟踪Kalman滤波的接触力估计方法和基于熵-自学习控制网络的关节角位置控制方法。建立了机械臂系统的关节驱动模型和动力学模型;基于时延估计理论,建立了4自由度机械臂系统的时延模型;在Kalman滤波中引入了渐消因子,使估计残差强行正交,从而提出了基于时延强跟踪Kalman的接触力估计方法。以接触力误差和关节位置误差为输入,以关节力矩为输出构建了自学习控制网络,并提出使用熵聚类确定网络结构和参数,从而设计了熵-自学习控制网络。经仿真验证,基于时延强跟踪Kalman的估计误差区间分布小于标准Kalman和文献[12]柔顺控制,且分布密度在0误差处极高;熵-自学习控制网络对期望轨迹的绝对跟踪误差和收敛时间远小于自学习控制网络和变阻抗阻尼控制。仿真结果验证了本文接触力估计方法和角位置控制方法的优越性。

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.

基于Kalman-BP的高边坡表面变形预测研究与应用

施工期中,泵站高边坡内部受力复杂,造成的表面变形可能导致滑坡坍塌等危及现场的安全作业事故,因此表面变形趋势预测具有实际工程意义。笔者选取4个现场监测的特征量——温度、锚杆应力、孔隙水压力和土压力作为模型的输入层,经过Kalman滤波对现场监测特征量的白噪音进行剔除,再进入BP神经网络进行训练,融合模型既能克服Kalman滤波的离散性现象,又提高了BP模型的泛化能力和收敛速度。将模型应用到重庆某泵站高边坡监测项目进行分析验证,结果表明:与传统BP神经网络相比,Kalman-BP神经网络针对H、Y、X三个方向累计变形量的训练迭代步数分别从1 086步减少至1 047步、1 090步减少至1 050步及1 080步减少至1 044步,预测H、Y、X三个方向累计变形量的均方根误差分别从0.974减小至0.684、1.037减少至0.564、0.982减少至0.526,模型的预测能力得到了提高,为高边坡安全作业提供了有效保障。

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.