Road vehicle state estimation using low-cost GPS/INS

Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55 kph).     

A novel distributed variational approximation method for density estimation in sensor networks

In this paper, a consensus filter based distributed variational Bayesian (CFBDVB) algorithm is developed for distributed density estimation. Sensor measurements are assumed to be statistically modeled by a finite mixture model for which the CFBDVB algorithm is used to estimate the parameters, including means, covariances and weights of components. This algorithm is based on three steps: (1) calculating local sufficient statistics at every node, (2) estimating a global sufficient statistics vector using a consensus filter, (3) updating parameters of the finite mixture model based on the global sufficient statistics vector. Scalability and robustness are two advantages of the proposed algorithm. Convergence of the CFBDVB algorithm is also proved using Robbins–Monro stochastic approximation method. Finally, to verify performance of CFBDVB algorithm, we perform several simulations of sensor networks. Simulation results are very promising.     

改进卡尔曼滤波的融合型锂离子电池SOC估计方法

荷电状态(SOC)估计对于锂离子电池充放电优化控制、任务规划、可靠性提升等均具有重要价值,针对广泛应用的卡尔曼滤波(KF)一类方法存在的参数设置无具体标准、模型性能随工况环境改变而适应性降低等问题,提出一种噪声方差可变卡尔曼滤波方法(VVKF)的SOC估计算法,该算法每次迭代时估计并设定最适应当前系统状态的的噪声方差,克服了KF噪声方差初值依靠人为经验设定而造成精度下降的问题,同时采用最小二乘支持向量机作为KF的量测方程,通过建立样本库的方式克服电池型号以及工况改变对SOC估计精度的影响。采用马里兰大学CACLE中心锂离子电池数据集的实验证明了VVKF较KF性能的提升以及SOC估计的有效性。     

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。     

Fast Kalman-like optimal FIR filter for time-variant systems with improved robustness

In this paper, a fast Kalman-like iterative OFIR algorithm is proposed for discrete-time filtering of linear time-varying dynamic systems. The batch OFIR filter is re-derived in an alternative way to show that this filter is unique for such systems. A computationally efficient fast iterative form is found for the OFIR filter using recursions. It is shown that each recursion has the Kalman filter (KF) predictor/corrector format with initial conditions specified via measurements on a horizon of N nearest past points. In this regard, the KF is considered as a special case of the iterative OFIR filtering algorithm when N goes to infinity. Applications are given for the 3-state target tracking and three-degree-of-freedom (DOF) hover system. It has been shown experimentally that the proposed iterative OFIR algorithm operates much faster than the batch OFIR filter and has the computational complexity acceptable for real-time applications. It has also been demonstrated by simulations that an increase in the number of the states results in better robustness of the OFIR filter against temporary model uncertainties and in higher immunity against errors in the noise statistics.     

基于有偏卡尔曼的多传感器数据融合研究

推导出偏参数为矩阵形式的有偏卡尔曼滤波(BKF)的完整迭代过程,该算法在均方误差条件下优于卡尔曼滤波(KF),可以进一步提高估计的精度.将BKF与多传感器融合算法中的扩维融合和序贯式融合相结合,推导出多传感器扩维有偏卡尔曼滤波和多传感器序贯有偏卡尔曼滤波算法,并从理论上证明了多传感器序贯BKF融合在均方误差条件下优于扩...     

基于AEKF的车辆质量与道路坡度实时估计

针对车辆在实际行驶过程中外界噪声的统计特性无法已知的问题,以车辆纵向动力学模型为基础,提出了自适应扩展卡尔曼滤波(adaptive extended Kalman filter,简称AEKF)的车辆质量及道路坡度估计算法。以动态估计车辆系统中的质量与坡度为研究对象,引入了旋转质量换算系数,建立车辆纵向动力学系统的状态空间模型,考虑了不同时刻的档位匹配与行驶特殊工况的处理。对系统状态方程进行离散化处理,得到系统状态方程与系统测量方程,在扩展卡尔曼滤波(extended Kalman filter,简称EKF)的基础上引入带遗忘因子的噪声统计估计器,通过AEKF对状态方程与测量方程实时更新,进行在线估计和校正噪声统计值,从而解决系统的噪声时变问题。本研究算法与EKF算法估计及实测结果的对比分析表明,本研究算法能够很好地对车辆质量和坡度信号进行有效滤波和估计,在短时间内逐渐收敛并逼近实测值,从而能够合理有效地检测车辆在行驶过程中的状态信息。     

改进扩展卡尔曼滤波算法在智能足球机器人比赛中的应用

为了减少系统延时对高实时性机器人足球比赛的影响,在深入研究卡尔曼滤波算法原理的基础上,提出了一种改进的扩展卡尔曼滤波(EKF)算法,建立了足球机器人竞赛中小球的运动模型,通过仿真实验给出了小球运动状态的预测轨迹。实验结果表明,改进后的EKF算法可以有效地解决以往算法在高度非线性化区域的不稳定性等问题,同时改进后的算法提高了系统实时性,算法易于实现且预测效果较好。     

Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm

This paper addresses the problem of attitude estimation using low cost, small-sized inertial sensors under dynamic maneuvers. An adaptive complementary filter with fuzzy logic and simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed. By recognizing the situation of dynamic condition via fuzzy logic, the cut-off frequency of the complementary filter is determined adaptively under varying vehicle dynamics. Also, the SPSA algorithm is used to tune the parameters of fuzzy system. Simulation results based on the test data show that the proposed SPSA-based fuzzy complementary filter exhibits a significant performance improvement for attitude estimation during dynamic maneuvers.     

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。     

Parametric adaptive estimation and backstepping control of electro-hydraulic actuator with decayed memory filter

Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm.     

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。     

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。     

基于卡尔曼滤波及牛顿预测的角加速度估计方法试验研究

基于角位置测量的角加速度实时估计问题是机电系统控制中一个非常重要的问题,在分析现有的线性回归平滑牛顿法和卡尔曼滤波法的基础上,提出了一种新的基于卡尔曼滤波和牛顿预测相结合的角加速度估计方法。该方法旨在利用牛顿预测进一步增强卡尔曼滤波的预测能力,减小由于滤波造成的相位滞后,提高估计加速度与实测加速度的一致性。为了验证新方法的有效性,以直接驱动机器人作为试验对象,采用将估计加速度的频率特性与实测加速度相比较的方法,分别对上述三种估计算法进行了试验比较研究,从而为利用估计加速度(取代测量加速度)实现加速度反馈控制提供了试验依据。     

巡飞弹空中自适应快速初始姿态估计

为了解决巡飞弹空中上电后在无参考姿态条件下的初始姿态确定问题,采用低成本磁力计、陀螺仪和加速度计(MARG)传感器设计姿态航向参考系统(AHRS),并提出了一种自适应参考矢量权重的快速初始姿态估计(AFCF)算法。首先,提出了三轴传感器使用前的快速误差校准方法;然后,采用快速互补滤波算法进行姿态估计,分析了其权重函数对于初始姿态估计及收敛性等的影响;接着,提出自适应参考矢量权重及自适应姿态估计方法;最后,利用高精度MTI(Milliren Technologies,Inc)传感器数据对算法进行了验证,并在低成本MARG姿态航向参考系统中对算法进行了实现,对比了改进算法及扩展卡尔曼滤波(EKF)算法的性能。实验结果与分析表明:动态条件下采用MTI传感器数据,改进算法能够在初始时刻收敛,比快速互补滤波(FCF)算法提前约4s;解算精度约为±0.6°,初始时刻精度明显优于FCF;硬件测试则表明改进算法的处理时间为0.062ms,仅为EKF算法的1/9,解算精度约为±1.3°,能够满足姿态测量过程快速收敛、高精度、实时性等要求。     

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.     

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。     

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。     

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.     

Parameter estimation for a dual-rate system with time delay

This paper investigates the parameter estimation problem of the dual-rate system with time delay. The slow-rate model of the dual-rate system with time delay is derived by using the discretization technique. The parameters and states of the system are simultaneously estimated. The states are estimated by using the Kalman filter, and the parameters are estimated based on the stochastic gradient algorithm or the recursive least squares algorithm. When concerning state estimate of the dual-rate system with time delay, the state augmentation method is employed with lower computational load than that of the conventional one. Simulation examples and an experimental study are given to illustrate the proposed algorithm.