微间隙焊缝磁光成像卡尔曼滤波跟踪算法

在激光对接焊过程中,实时控制激光束准确对中焊缝是保证焊接质量的前提。针对紧密对接激光焊,研究一种用于微间隙(不大于0.1 mm)焊缝位置识别及跟踪的磁光成像卡尔曼滤波算法。采用磁光传感器实时获取焊接区域的微间隙焊缝磁光图像序列,利用微间隙焊缝磁光图像的灰度梯度特征提取焊缝位置坐标。以焊缝位置及位移量构成状态矢量,建立描述焊缝位置的状态方程和测量方程。同时,假设系统动态噪声和测量噪声为零均值随机分布的高斯白噪声,建立噪声环境下的卡尔曼滤波跟踪算法,计算最小均方差条件下焊缝中心最优预测值,减小系统噪声和过程噪声对焊缝位置测量的影响。试验结果显示,该方法能有效实现激光对接焊微间隙焊缝位置的识别与跟踪。     

基于多基站IMMKF-DOA辅助车辆协同定位算法研究

针对传统卡尔曼滤波算法在进行车辆实时运动过程中难以精准定位问题,提出一种基于运动状态自适应的交互多模型卡尔曼滤波(Interacting multiple model Kalman filter,IMMKF)与多基站到达方向(Direction-of-arrival,DOA)相融合进行车辆位置实时估计算法。基于无偏估计器对测量噪声协方差进行实时更新并将其嵌入标准卡尔曼滤波算法中实现自适应交互多模型卡尔曼滤波。针对车辆不同运动状态及动态行驶环境对车辆定位估计精度的影响,构建自适应交互多模型卡尔曼滤波器与多基站信息融合算法进行车辆位置实时估计,考虑不同车速与不同基站数等行驶工况下车辆定位精度的变化趋势,实现车辆实时位置的准确估计。利用PreScan-Simulink联合仿真平台进行虚拟仿真验证和实车试验验证。结果表明,基于交互多模型卡尔曼滤波与到达方向角的融合算法相对标准的卡尔曼滤波估计精度高,较好地改善了传统单一模型的卡尔曼滤波算法在进行车辆实时运动状态估计过程中精准定位问题,实车试验验证了提出算法对车辆定位精度较传统卡尔曼滤波算法的精度提高了一个数量级,实现了更精确的车辆位置估计。     

基于卡尔曼滤波的焊缝检测技术研究

提出一种基于卡尔曼滤波技术的电弧焊焊缝检测新方法。利用视觉传感器获取弧焊区熔池图像,并抽取图像质心作为描述焊缝位置的特征矢量,建立图像质心状态方程和测量方程。在有色噪声模型的基础上,应用卡尔曼滤波对图像质心位置和质心位移进行状态估计,得到最小均方差条件下的焊缝位置最佳预测值,从而减小过程噪声和测量噪声引起的焊缝位置测量偏差,实现弧焊过程中焊缝位置的精确检测。计算机仿真及实际焊接试验结果验证了该方法的有效性。     

四轮驱动电动汽车质心侧偏角与轮胎侧向力非线性鲁棒融合估计

针对四轮驱动电动汽车质心侧偏角和轮胎侧向力难以直接测量的问题,考虑系统未建模的动态特性、模型参数摄动、系统过程噪声及测量噪声等因素,提出了一种基于遗忘因子递归最小二乘法(FFRLS)与鲁棒容积卡尔曼滤波(RCKF)的联合估计方法。基于FFRLS法对整车质量进行实时估计,并将极大值背景下的估计误差最小化嵌入标准容积卡尔曼滤波（CKF）以实现RCKF,提出了联合估计算法的改进策略,有效提高了复杂工况下滤波对模型参数摄动以及未建模噪声的抗干扰能力,可以实现质心侧偏角与轮胎侧向力的精准估计。在CarSim/Simulink联合仿真环境下,采用不同工况验证了算法的准确性、鲁棒性和抗干扰性。在四轮驱动电动汽车实车平台上分析了算法的有效性。研究结果表明,所提方法比RCKF和CKF精度更高,解决了复合工况下四驱电动汽车质心侧偏角和轮胎侧向力的联合估计问题。     

Comparison of Linearized Kalman Filter and Extended Kalman Filter for Satellite Motion States Estimation

The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.     

自适应卡尔曼滤波在无刷直流电机系统辨识中的应用

为了有效抑制量测噪声特性变化对系统辨识精度的影响以获得准确的无刷直流电机模型,提出了一种采用自适应卡尔曼滤波算法的无刷直流电机系统辨识方法。通过计算新息理论方差的极大似然最优估计,并将其引入卡尔曼滤波算法中修正滤波增益来抑制量测噪声特性变化对辨识结果的影响,使该滤波算法实现对模型参数的准确估计,提高辨识精度。实验结果表明,在量测噪声特性变化的情况下,该算法能够准确跟踪实际量测噪声特性的变化,参数估计平滑,相对于目前系统辨识广泛采用的带有遗忘因子的递推最小二乘算法,输出误差的均方根值减小了73.5%。该算法简单易行,计算量小,辨识结果可以很好地描述系统行为,便于在工程实践中应用。     

移动机器人自适应抗差无迹粒子滤波定位算法

针对机器人定位过程中传感器感知信息存在野值,加剧粒子退化,导致机器人状态参数滤波值失真,甚至出现定位失败的问题,提出一种机器人自适应抗差无迹粒子滤波定位算法。在重要性采样阶段利用无迹卡尔曼滤波产生优选的建议分布函数,降低系统估计误差,同时有效提升系统的抗噪声能力。同时利用抗差估计原理构造抗差方差分量统计量,并由该统计量引入的自适应因子调节增益矩阵,减弱野值对滤波的影响。实验结果表明,当观测数据中存在野值时,该算法能够有效地控制观测异常误差的影响,定位精度得到了很大提高,并在不同系统噪声和观测噪声方差下,具有较强的鲁棒性和实时性。     

基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计

针对传统容积卡尔曼滤波算法在进行车辆关键状态估计时要求噪声统计特性已知的问题,提出一种噪声自适应容积卡尔曼滤波（Noise adaptive cubature Kalman filter, NACKF）算法来进行车辆关键状态的估计。基于次优无偏极大后验估计器对量测噪声协方差进行实时更新并将其嵌入到标准容积卡尔曼算法中实现自适应容积卡尔曼滤波。针对车辆不同子系统间耦合特性对滤波精度的影响,构建双重自适应容积卡尔曼滤波器分别进行侧向力与质心侧偏角的估计,两者在估计过程中互为输入构成闭环反馈,利用分布式模块化结构弱化系统耦合特性对估计精度的影响,实现轮胎侧向力与质心侧偏角的实时准确估计。利用Simulink-Carsim联合仿真平台进行仿真验证和实车试验验证。结果表明,基于双重自适应容积卡尔曼滤波的估计算法相对标准容积卡尔曼滤波估计精度更高,较好地改善了传统容积卡尔曼滤波器在噪声先验统计特性未知条件下非线性滤波精度下降的问题。     

基于Sage窗的自适应Kalman滤波用于钟差预报研究

钟差预报是时间保持工作中的一项关键技术。Kalman算法作为一种最优预报算法,具有实时性的特点,在时间保持工作中得到了广泛的应用。但是由于经典Kalman算法需要准确确定模型随机误差和测量误差,否则状态估计会引入一定的误差,在原子时算法中表现为原子钟噪声和钟差测量噪声。原子钟的噪声参数值通常是通过Allan方差估计,若估计不够准确,Kalman预报将会出现误差。通过研究基于Sage窗的自适应Kalman预报算法,实时修正状态模型误差。利用自适应因子调整状态预测协方差阵有效降低了模型误差,提高了预报精度,最后通过两台氢原子钟和两台铯原子钟的实测数据验证了算法的有效性。     

Evaluation of linear Kalman filter processing geodetic kinematic measurements

The purpose of this paper is to assess the efficiency of the linear Kalman filter as a method for the estimation of kinematic process observed with electronic tacheometer. On the assumption that the kinematic process is observed with only one measurement system there is no redundancy of the measurements. For evaluation of the unknown system state and its statistics in real time, other methods such as filters have to be used instead of classical geodetic adjustment. In this contribution, the efficiency of the three-dimensional linear Kalman filter model, i.e., continuous Wiener process acceleration model, in combination with the law on transfer of variances and covariances, is controlled using a known reference trajectory and statistical tests. The results of numerical tests confirmed the appropriateness of the model to evaluate geodetic kinematic measurements, where no redundant observations are available.     

基于递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的车辆状态估计

针对汽车状态估计中模型参数的变化和观测噪声的时变特性,提出了递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的汽车状态估计算法。为实现模型参数与观测噪声的实时更新,建立了基于三自由度非线性车辆动力学模型的算法,首先利用递推最小二乘法对汽车的总质量进行估计,其次建立了模糊控制器对扩展卡尔曼滤波的观测噪声进行实时跟踪。在搭建的CarSim与MATLAB/Simulink联合仿真平台中验证了该算法的有效性,结果表明该算法估计精度高于传统扩展卡尔曼滤波算法,研究结果为汽车的主动安全控制提供了理论支持。     

Maximum correntropy square-root cubature Kalman filter with application to SINS/GPS integrated systems

For a nonlinear system, the cubature Kalman filter (CKF) and its square-root version are useful methods to solve the state estimation problems, and both can obtain good performance in Gaussian noises. However, their performances often degrade significantly in the face of non-Gaussian noises, particularly when the measurements are contaminated by some heavy-tailed impulsive noises. By utilizing the maximum correntropy criterion (MCC) to improve the robust performance instead of traditional minimum mean square error (MMSE) criterion, a new square-root nonlinear filter is proposed in this study, named as the maximum correntropy square-root cubature Kalman filter (MCSCKF). The new filter not only retains the advantage of square-root cubature Kalman filter (SCKF), but also exhibits robust performance against heavy-tailed non-Gaussian noises. A judgment condition that avoids numerical problem is also given. The results of two illustrative examples, especially the SINS/GPS integrated systems, demonstrate the desirable performance of the proposed filter.     

基于自适应Kalman滤波的SAW测温数据纠错方法

在无源无线SAW测温系统实际应用中,阅读器接收到的信号往往受到其所处环境电磁波的干扰。这些干扰将会使阅读器得到错误的测量数据。温度变化趋势和测量噪声时变的特点也给系统建模以及噪声估计带来了困难。针对实际应用中存在的问题,在Kalman滤波的基础之上,提出了一种新的自适应算法。该算法采用多项式预测的方法建立温度测量的时变系统模型,根据当前及历史测量值,自行调整预测模型参数,避免因模型不准确造成Kalman滤波效果严重下降的问题;通过对测量数据小波变换的方法,实时估计测量数据噪声方差,克服未知观测噪声的条件下精度下降的问题;当测量数据受到干扰时,测量值与纠错值之间的差值不满足高斯分布,通过对差值统计特性的分析,对测量数据进行错误数据判别与剔除,有效地抑制干扰对温度测量的影响。将这种自适应Kalman滤波算法应用到无源无线SAW测温系统中,无源无线SAW温度传感器测温实验的结果验证了该算法能有效地纠正粗大误差,提高测量系统的精度。     

复杂交通流下基于卡尔曼滤波的多目标全生命周期状态估计

针对复杂行车环境下噪声干扰和车辆行车过程中状态变化导致交通场景中目标状态估计精度低的问题,以毫米波雷达 为检测传感器,提出涵盖参数初始化和在线更新的基于卡尔曼滤波的多目标全生命周期状态估计方法。 首先,建立交通流下多 目标运动状态的卡尔曼滤波状态估计模型;基于此,一方面提出基于数据驱动的卡尔曼滤波观测噪声协方差矩阵初始化的新方 法,另一方面采用变分贝叶斯方法对卡尔曼滤波参数进行在线更新,以此提高多目标状态估计精度;最后,在算法实现步骤的基 础上,利用实车数据开展测试验证工作。 实验结果表明,方法的目标状态估计均方误差为 0. 153,相较于传统卡尔曼滤波减小 了 36. 2% ,证明所提出方法对提升车辆感知精度的有效性。     

抗野值自适应卫星/微惯性组合导航方法

针对在复杂城市环境下卫星导航系统(GNSS)定位定速存在野值,导致GNSS/微惯性(MEMS-INS)组合导航状态参数滤波估计精度恶化,甚至滤波发散的问题,提出了一种抗野值自适应GNSS/MEMS-INS组合导航算法,以提高组合导航精度和可靠性。该算法利用Allan方差分析建立较为精确的MEMS器件噪声模型,有效降低模型异常和状态扰动的影响。同时利用新息序列构造观测异常检验统计量,并根据该统计量构造自适应新息加权因子调节滤波增益矩阵,削弱观测野值对状态估计的不良影响。实验结果表明,该算法能够有效地控制GNSS定位定速异常的影响,具有较强的实时性和容错性。相比于传统算法,车载定位、定速和定姿精度分别提升35.78%、60.19%和82.41%,验证了本文算法的有效性和实用性。     

基于组合模型的锂电池参数辨识和电池荷电状态在线联合估计

针对电池荷电状态(SOC)估算过程中开路电压与SOC之间的迟滞效应以及充放电电流和端电压中噪声的影响，提出了基于组合模型的Frisch 方案双滤波（FSDF）法。先通过一阶RC等效电路模型结合Preisach离散模型建立新的模型，随后采用Frisch 方案对模型的输入输出进行噪声方差估计，滤除部分输入输出噪声，最后使用扩展卡尔曼滤波结合无迹卡尔曼滤波进行参数实时更新和电池单体SOC估算。实验证明，FSDF方法对锂电池SOC估算结果与Frisch方案递推最小二乘无迹卡尔曼滤波法等其他方法相比，具有精度高、鲁棒性好等特点。     

一种任意相关噪声异类传感器测量融合算法

基于测量融合理论提出了一种任意相关噪声异类传感器非线性系统目标跟踪算法。该算法在构造跟踪滤波器时，在增益阵中引入了测量噪声与系统噪声相关以及测量噪声之间相关项，增加了描述多传感器融合系统的信息量。仿真结果表明，由于考虑了过程噪声和测量噪声以及测量噪声之间的相关性，测量融合算法比噪声不相关融合算法具有更好的跟踪性能，并且航迹跟踪的精度得到了改善。     

Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults

In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.     

Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system

GPS/INS integrated system is very subject to uncertainties due to exogenous disturbances, device damage, and inaccurate sensor noise statistics. Conventional Kalman filer has no robustness to address system uncertainties which may corrupt filter performance and even cause filter divergence. Based on the INS error dynamic equation, a robust Kalman filter is analyzed and applied in loosely coupled GPS/INS integration system. The norm bounded robust Kalman filter, with recursive form by solving two Riccati equations, guarantees a estimation variance bound for all the admissible uncertainties, and can evolve into the conventional Kalman filter if no uncertainties are considered. This paper will analyze the suitable case for the robust Kalman filter in GPS/INS system, the filter characteristics including parameter setting, parameter meaning, and filter convergence condition are discussed simutaneously. The robust filter performance will be compared with conventional Kalman filter through simulation results.