5基于改进型采样点卡尔曼滤波的矿用电池SOC估计

为了实时、准确地估计矿用电池SOC值,通过采用加权统计线性回归法实现模型函数线性化,将采样点卡尔曼滤波技术应用到矿用电池SOC估计中.针对有限的电池管理系统资源,基于电池状态观测复合模型的状态方程线性和观测方程非线性的特点,提出了将标准卡尔曼滤波和采样点卡尔曼滤波组合的非线性滤波算法;为了使得该算法具有应对突变状态的强跟踪能力和应对模型不准确的鲁棒性,引入了奇异值分解,采用特征协方差矩阵代替误差协方差矩阵,并基于强跟踪原理引入了次优渐消因子.仿真结果表明,基于改进型采样点卡尔曼滤波的矿用电池SOC估计算法兼顾估计精度和运算量,并具有跟踪突变状态和应对模型不准确的鲁棒性,完全适用于资源有限的矿用电池SOC估计;可见,该算法具有良好的实际应用价值.     

粒子滤波算法在ECT图像重建中的应用

针对电容层析成像技术(ECT)的图像重建质量精度较低的问题,提出了一种基于粒子滤波的ECT图像重建方法。首先,分析了ECT图像重建基本原理,以系统状态估计的方式描述了ECT图像重建最优解的搜索过程,并建立了状态空间模型。然后,以线性反投影(LBP)算法的图像重建结果作为初始状态,利用测量信息对从状态空间中获取的随机样本进行最优加权,以获得重建图像的最小方差估计。最后,对5种不同的流型进行了仿真实验。实验结果表明,利用本文方法获得的重建图像误差平均值为42.93%,相关系数平均值为0.813 9,比LBP算法、Landweber迭代算法和IMN-SNOF算法得到的相应指标要好。本文方法是一种有效、精度较高的ECT图像重建方法,为ECT图像重建技术提供了新的途径和手段。     

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.     

Spacecraft attitude determination using a decoupling filter

In this paper, an algorithm for real-time attitude estimation of spacecraft motion is investigated. For efficient computation, the decoupling filter presented in this paper is accomplished by a derived pseudo-measurement from the given measurement and the decoupled state in the original system. However, the proposed decoupling filter contains model errors due to coupling terms in the system. Therefore, we develope an attitude determination algorithm in which coupling terms are compensated through an error analysis. The attitude estimation algorithm using the state decoupling technique for real-time processing provides accurate attitude determination capability under a highly maneuvering dynamic environment, because the algorithm does not have any bias errors from a truncation, and the covariance of the estimator is compensated by nonlinear terms in the system. To verify the performance of the proposed algorithm vis-a-vis the EKF (extended Kalman filter), and the nonlinear filter, simulations have been performed by varying the initial values of the state and covariance, and measurement covariance. Results show that the proposed algorithm has consistently better performance than the EKF in all of the ranges of initial state values and covariance values of measurement, and it is as accurate as the nonlinear filter. However, the convergence speed of the nonlinear filter is faster than the proposed algorithm because of the pseudo-measurement model errors in the proposed algorithm. We show that the computational time of the proposed algorithm is improved by about 23% over the nonlinear filter.     

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。     

S-修正的自适应卡尔曼滤波与模糊卡尔曼滤波相结合的汽车状态估计算法

精确的汽车状态信息的获取是汽车动态控制系统正常工作的前提。建立了二自由度汽车动力学模型,提出了将S-修正的自适应卡尔曼滤波与模糊卡尔曼滤波相结合进行汽车关键状态估计的方法。模糊卡尔曼滤波利用所设计的模糊控制器通过实时监测信息实际方差与理论方差的比值,实现对时变量测噪声的协方差矩阵的实时在线估计,提高了算法在时变量测噪声情况下的鲁棒性;S-修正的自适应卡尔曼滤波算法基于滤波不发散理论推导得出实时修正因子S,进而对估计误差协方差矩阵直接加权。两种方法的结合在总体上提高了在汽车动力学系统过程噪声与量测噪声协方差矩阵不准确情况下算法的鲁棒性与估计精度,最后通过基于ADAMS的虚拟试验验证了该方法的有效性。     

采用自适应无迹卡尔曼滤波的卫星姿态确定

针对现有算法卫星姿态确定中模型参数估计不准确,系统存在外界干扰下稳定性差和跟踪精度不足的问题,提出一种自适应无迹卡尔曼滤波算法,对卫星三轴姿态进行估计.首先分析了陀螺和星敏组合定姿的工作原理,然后推导了以误差四元数为状态变量的卫星姿态运动学方程.滤波过程中,该算法引入自适应矩阵,对量测噪声协方差矩阵进行调整;依据滤波发...     

An attitude determination algorithm for a spacecraft using nonlinear filter

In this paper, the algorithm for a real time attitude estimation of a spacecraft motion is investigated. The proposed algorithm for attitude estimation is the second order nonlinear filter form not containing truncation error in estimation values. The proposed second order nonlinear filter has improved performance compared with the EKF (extended Kalman filter), because the algorithm does not contain any truncation bias and covariance of the estimator is compensated by the nonlinear terms of the system. Therefore, the proposed second order nonlinear filter is a suboptimal estimator. However, the proposed estimator requires a lot of computation because of an inherent nonlinearity and complexity of the system model. For more efficient computation, this paper introduces a new attitude estimation algorithm using the state divided technique for a real time processing which is developed to provide an accurate attitude determination capability under a highly maneuverable dynamic environment. To compare the performance of the proposed algorithm with the EKF, simulations have been performed with various initial values and measurement covariances. Simulation results show that the proposed second order nonlinear algorithm outperforms the EKF. The proposed algorithm is useful for a real time attitude estimation since it has better accuracy compared with the EKF and requires less computing time compared with any existing nonlinear filters.     

基于模糊卡尔曼滤波器的锂电池荷电状态与健康状态预测

针对当前锂电池荷电状态(State of charge, SOC)与健康状态(State of health, SOH)预测精度较低的问题,提出了一种基于模糊卡尔曼滤波器的预测方法。采用非线性二阶电阻电容模型表示锂电池,并通过最小二乘误差优化算法对模型参数进行估计,从而更准确地确定蓄电池容量作为SOH值的基础。扩展卡尔曼滤波器(Extended Kalman filter, EKF)可在初始SOC值未知的情况下对其进行准确预测,而模糊逻辑有助于消除测量和过程噪声。仿真结果表明,在城市测功机驱动计划期间(Urban dynamometer drving schedule, UDDS)测试中最大的SOC估算误差是0.66%;通过离线更新卡尔曼滤波器,可对电池容量进行估计,结果表明,最大估计误差为1.55%,从而有效提高了SOC值的预测精度。     

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。     

Clock state estimation with the Kalman-like UFIR algorithm via TIE measurement

We show that recently designed the p-shift unbiased finite impulse response (UFIR) iterative algorithm is highly efficient in applications to clock state estimation via measurement of the time interval error (TIE). The algorithm is Kalman-like, but does not involve noise statistics and initial errors. Its crucial property is that the estimate becomes optimal in the minimum mean square error sense when the estimator memory is large that is typical for clocks. Examples are given for state estimation in an ovenized crystal clock and error prediction in a master clock. Based upon the experimental studies, we show that this algorithm outperforms the Kalman filter requiring the clock noise covariance matrix that is hard to specify correctly even in white Gaussian approximation.     

基于自适应模拟退火及 LM 联合反演算法的 ECT 图像重建

为解决电容层析成像技术(ECT)中图像重建的非线性和病态性问题,提出了一种自适应模拟退火-Levenberg Marquardt (ASA-LM)联合反演算法。 改进了标准模拟退火(SA)算法的新解生成策略、能量函数的定义及退火策略,并结合 LM 的直接局 部搜索方法联合反演 ECT 图像重建问题。 同时,利用 Savitzky-Golay (SG) 滤波对 ECT 图像重建所需电容数据进行平滑处理以 提高其信噪比。 最后,进行仿真及静态实验,并与线性反投影(LBP)、Landweber 迭代及标准 SA 算法进行了比较。 结果表明,与 其他 3 种算法相比,ASA-LM 算法收敛速度快、图像重建质量明显提高,边缘信息保真度高,重建图像的平均相对误差为 0. 331 1,平均相关系数为 0. 933 1。     

一种小型无人机无源目标定位方法及精度分析

为了提高小型无人机无源目标定位的精度,设计了一种新的目标定位算法。首先确定目标定位过程中的坐标转换关系并推导出视轴角的计算模型;然后,利用光电侦察平台锁定跟踪目标的特性,提出了对同一目标点多次测量的目标定位框架,建立了系统状态方程和测量方程,考虑到测量方程的非线性,将无迹卡尔曼滤波应用于目标位置估计;最后,针对加性高斯白噪声的非线性目标定位系统,推导出理论上的定位误差的克拉美-罗下限。仿真结果表明,该算法具有较高的目标定位精度,滤波器估计误差均方差已逼近非线性系统的克拉美-罗下限。现场试验结果表明,在离地面约1000 m的空中,无人机对地面目标定位精度可达8.1 m。该算法易于部署,可操作性强,具有较大的实用价值。     

基于力觉遥示教焊缝辨识信号数值滤波技术

在基于力觉的遥示教过程中,为克服由于机器人的振动、焊缝表面粗糙不平和焊机电磁场干扰等因素造成焊缝的辨识力信号不稳,基于受力信号变化焊缝辨识模型分析,提出用卡尔曼滤波递推方法对焊缝辨识受力信号进行的滤波处理,建立焊缝受力信号滤波数学模型.通过模型的状态方程、观测方程、状态预报滤波、滤波增益矩阵和预报状态的协方差矩阵,完成对下一时刻受力信号准确预报.试验表明,卡尔曼滤波使焊缝受力信号状态估计的误差变小,增加焊缝辨识受力信号检测精度,进而可以提高遥控焊接遥示教焊缝辨识精度.     

基于U曲线法的油气润滑ECT系统图像重建病态优化

采用U曲线法确定油气润滑ECT系统图像重建中的正则化参数,分析正则化处理后灵敏度矩阵的病态性;通过LBP算法和Tikhonov正则化算法分别对油气润滑ECT系统管道截面进行第一次图像重建;对第一次重建图像的灰度分布矩阵进行门限滤波阈值的优化,并对管道截面进行二次图像重建。结果表明:相较于L曲线法,U曲线法选取的正则化参数在削弱灵敏度矩阵病态程度方面的作用显著;第一次图像重建中,图像重建质量有较大改善;门限滤波阈值优化后的二次图像重建中,图像重建质量进一步提高。研究表明U曲线法确定的正则化参数和门限滤波阈值优化有助于提高油气润滑ECT系统的图像重建质量。     

非线性状态空间方法辨识电液伺服控制系统

针对回归神经网络辨识和建立非线性动态系统模型的问题,研究非线性状态空间描述的回归神经网络数学模型。讨论极小均方误差网络训练收敛准则,通过研究Kalman 滤波估计公式中的随机变量,提出一种参数增广的回归神经网络非线性状态方程,无导数的Kalman滤波器用于增广参数估计,人工白噪声强迫网络学习,更新网络权值,避免了扩展Kalman滤波器计算Jacobian信息和基于递度学习算法收敛慢的问题。在电液伺服系统辨识建模的应用中表明,回归神经网络较好地跟踪了液压油缸压力变化,与扩展Kalman滤波估计学习算法相比,新的算法具有较快的收敛和精度。     

最小化预测残差的图像序列压缩感知

提出了一种最小化预测残差的图像序列压缩感知算法以实现高速相机输出图像的实时压缩.首先,在编码端仅使用映射矩阵对原始输出图像进行压缩,将压缩得到的观测向量通过信道传输到解码端.接着,在解码端对相邻帧进行运动估计和运动补偿,得到一幅待重建图像的预测图像,利用压缩感知算法对原始图像和预测图像之间存在的预测残差图像进行重建.最后,用迭代的方法优化预测残差图像的重建结果,直到连续两次的重建结果之差小于设定阈值,从而获得重建的原始图像.采用DALSA公司的CR-GEN0 H6400相机进行的实验表明,该算法可以实现1 000 frame/s图像的实时压缩,并且图像重建质量比独立地重建每张图像至少提高了2～6 dB,有效地实现了对高速相机输出图像的实时压缩与高质量重建.     

基于加权SVD截断共轭梯度的ECT图像重建算法

针对电容层析成像技术中的“软场”效应和病态问题,基于灵敏度矩阵的奇异值分解理论,提出了一种加权SVD截断共轭梯度的ECT图像重建算法,给出了算法的数学模型,完成了算法的收敛性分析和证明,并将其应用在电容层析成像系统的图像重建中。仿真和实验结果表明,同LBP算法和CG算法相比,该算法有成像效果好,成像速度快,易于实现等特点。     

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。     

A combined nonlinear contrast image reconstruction algorithm under inexact point-spread function

The problem of image reconstruction is considered for the case when the right-hand side of the 2D integral equation and the point-spread function (the integral equation kernel) are given with random errors. A stable image reconstruction algorithm is proposed. It is a combination of a regularizing algorithm for solving an integral equation (frequency filtering) and a local nonlinear filter (spatial filtering). Characteristics of the 2D point-spread function of the regularizing algorithm are introduced. The regularization parameter is chosen according to the required regularizing algorithm resolution. For eliminating the random reconstruction error, the regularized solution is subjected to nonlinear local filtering that preserves high-frequency information components of the image.