EKF和PF算法及其在目标跟踪中的应用

基于贝叶斯滤波的目标跟踪原理,介绍了扩展卡尔曼滤波(Extended Kalman Filter,EKF)和粒子滤波(ParticleFilter,PF)的基本思想和算法实现步骤。在非线性环境下对比分析了EKF算法和PF算法的估计精度,并给出两种方法的适用条件。EKF算法采用Taylor展开的线性变换来近似非线性模型,而PF算法采用一些带有权值的随机样本来表示所需要的后验概率密度。仿真结果表明,在强非线性非高斯环境下,PF算法的跟踪性能远优于EKF算法,当系统非线性强度不大时,EKF算法和PF算法的估计精度相差不大,但PF算法计算复杂,跟踪时间长,实时性差。     

粒子滤波技术研究和分析

非线性非高斯状态空间模型的最优估计问题在信号处理、自动控制、金融、无线通讯等领域具有重要的应用,粒子滤波技术通过非参数化的蒙特卡罗模拟方法来实现递推贝叶斯滤波,适用于任何能用状态空间模型表示的非线性系统,滤波精度可以逼近最优估计,其有效性已经得到各领域研究人员的极大认可,基本粒子滤波算法存在的最大问题是粒子退化,针对这一问题,对权值退化、重要性函数选取、重采样等影响粒子滤波器性能的关键技术进行深入研究。     

基于粒子滤波的多枚声呐浮标联合跟踪定位算法

由于被动声呐浮标目标测量源的不确定性以及位置解算方程的非线性,声呐浮标联合跟踪定位面临着非线性非高斯问题,提出一种基于粒子滤波的多枚声呐浮标联合跟踪定位算法。该算法将最优贝叶斯滤波与蒙特卡洛随机采样方法相结合,在更广义的条件下实现了目标最优状态估计。算法仿真结果表明,可以较大程度的提高目标位置估计精度。     

机器人液压驱动器摩擦力估计的一种新方法

摩擦力对液压系统的控制性能有很大影响。为了很好地控制液压驱动器,需要对液压缸摩擦力进行建模。本文提出了一种基于卡尔曼滤波器的摩擦力参数辨识方法：用高斯牛顿法拟合出非线性系统的模型;以活塞位移、速度和扭矩作为参考,用Extended Kalman Filter估计液压缸的压力和摩擦力。经试验验证,该方法估计出的压力和摩擦力与试验数据比较接近。该摩擦力辨识方法的优点为：可在线估计摩擦力;可用于系统有噪音的情况;不需要在线测量液压缸压力。     

一种改进的粒子群优化粒子滤波算法

粒子群优化粒子滤波方法容易陷入局部最优,针对这一问题,提出一种改进的粒子群优化粒子滤波算法,该算法对惯性权重和位置更新采用模糊控制,增强粒子全局搜索的能力,防止粒子陷入局部最优,提高估计精度。     

一种信标水声定位的改进扩展卡尔曼滤波算法

水声测距误差通常偏离高斯分布,纯距离扩展卡尔曼滤波(Extended Kalman Filter,EKF)定位跟踪算法误差较大。在将测距噪声分为高斯分量和非高斯缓变分量的基础上,提出了一种改进的扩展卡尔曼滤波EKF算法(Improved Extended Kalman Filter,IEKF)和初值选取方法。利用仿真实验和湖试对IEKF算法进行了验证,结果表明IEKF算法能够对测距偏差进行跟踪补偿,定位精度明显优于常规EKF算法。     

基于改进 Kalman 滤波分布式航天器相对位置的解算

用Kalman滤波算法解算分布式航天器相对位置,与点估计法比较可提高定位精度．但传统Kalman滤波常会出现滤波发散的现象,影响滤波精度．通过研究克服传统Kalman滤波发散的方法,设计了一种改进的Kalman滤波算法,主要从初始值的设计及自适应调节两方面进行了改进．根据分布式航天器相对位置的状态方程和观测方程,利用该算法对相对位置进行了仿真解算．仿真结果表明,该算法用于分布式航天器相对位置的解算中三轴的滤波精度都可达到厘米级,与传统Kalman滤波算法相比,该算法可有效地克服滤波发散,提高滤波精度．     

基于高斯粒子滤波的当前统计模型跟踪算法

对于非线性系统估计问题,高斯粒子滤波器可以获得近似最优解,与粒子滤波器相比其优点是不需要重采样步骤和不存在粒子退化现象.采用高斯粒子滤波代替当前模型自适应跟踪算法中的卡尔曼滤波,将高斯粒子滤波与当前统计模型的优点相结合,提出了一种新的当前统计模型自适应跟踪算法,用于非线性非高斯系统的机动目标跟踪.MonteCarlo仿真表明,该算法跟踪精度优于标准的交互多模型算法和当前统计模型自适应跟踪算法,实时性好于交互多模型粒子滤波算法.     

基于改进PF算法的锈蚀RC结构抗弯承载力预测方法

为提高锈蚀钢筋混凝土(RC)结构抗弯承载力评估精度，该文综合考虑锈蚀RC结构几何尺寸、钢筋截面积及力学性能、混凝土强度、粘结性能等因素，提出了基于改进粒子滤波(PF)算法的抗弯承载力模型参数更新及预测方法。通过生成大量的粒子以表征承载力退化过程中模型参数的不确定性，从选择不同建议密度函数的角度改进PF算法以解决传统PF算法中粒子退化的问题，分别采用PF、扩展粒子滤波(EPF)、无迹粒子滤波(UPF)算法对模型参数进行估计与更新，实现了锈蚀RC结构抗弯承载力的有效预测。结果表明：随着钢筋锈蚀率的增加，RC结构的抗弯承载力逐渐降低。基于改进PF算法的锈蚀RC结构抗弯承载力预测方法因考虑了模型参数更新使得预测结果更接近试验数据。基于EKF和UKF的改进PF算法可有效抑制粒子退化，其预测精度较PF算法更高；锈蚀RC结构抗弯承载力预测精度随着训练数据及粒子数的增加而提高。     

非线性系统的小波分频的扩展Kalman滤波

基于噪声的小波变换特点,结合量测的多尺度分解和扩展Kalman滤波(EKF),提出了一种小波“最佳”尺度分解的分频EKF滤波算法。该算法依据小波变换模功率谱选择最佳小波分解尺度,并将小波多尺度分解去噪和分频EKF滤波结合起来。对实际中含强噪声的非线性动态系统进行状态估计效果较好。Monte-Carlo仿真表明,与普通EKF滤波相比,本文算法的滤波精度平均提高约10%。     

基于RFF及GWO-PF的锂电池SOC估计

为了提高锂电池剩余电量估计的准确性,提出一种在线参数辨识与改进粒子滤波算法相结合的锂电池SOC估计方法。针对粒子滤波中的粒子退化问题,引入灰狼算法,利用灰狼算法较强的全局寻优能力优化粒子分布,保证粒子多样性,有效抑制粒子退化现象,提高滤波精度。采用带遗忘因子的递推最小二乘法实时更新模型参数,并与改进粒子滤波算法交替运行,进一步提高SOC的估计精度。实验结果表明,改进算法的平均估计误差始终保持在±0.15%以内,相比扩展卡尔曼滤波与无迹卡尔曼滤波算法,在电池SOC估计上有更高的估计精度与稳定性。     

地磁旋转弹滚转角及角速率的估计方法

针对高转速的旋转弹等制导弹药滚转角及滚转角速率实时获取的问题,提出一种基于地磁信息的滚转角及滚转角速率的实时快速估计方法。首先根据旋转弹的轴向滚转运动特性,利用卡尔曼滤波算法实时估计弹丸在轴向高速旋转的状态下的滚转角以及角速率,考虑弹载实时应用需求,在卡尔曼滤波的基础上进一步采用α-β-γ滤波来提高估计算法的实时性。通过仿真数据以及半物理验证,结果表明,相比于直接用卡尔曼滤波估计,采用α-β-γ滤波估计的时间缩短一个数量级,明显提高算法的实时性和快速性。同时该方法估计的滚转角误差在3°以内,比系统直接解算的滚转角准确度提高1倍;滚转角速率的估计准确度在5°/s以内,比直接求导准确度提高6倍以上,满足常规旋转弹的需求。     

基于模糊自适应Kalman滤波的机械手动态图像雅可比矩阵辨识

提出了一种改进的基于模糊自适应Kalman滤波的动态图像雅可比矩阵辨识方法.该方法在机器人参数和滤波参数未知而且视觉成像模型动态变化的情况下,通过模糊逻辑自适应控制器在线监测滤波残差均值和残差协方差误差,对过程噪声参数Q和量测噪声参数R进行自适应调节,实现了未知环境下动态图像雅可比矩阵的稳定辨识.通过微装配机械手运动实验验证了该方法的有效性.     

ADAPTIVE FILTERING IN STRUCTURAL DYNAMICS

The Kalman filter has been shown to be ideally suited to both the state and parameter estimation problems in structural dynamics. However these exploratory works on the application of Kalman filtering to structural engineering problems, in general only give suboptimal results, relying on assumed statistics to describe the noise sequences. Optimality however can be achieved by adapting onto these statistics (or the filter gain), using output from the filter equations to feed the adaptive algorithm. The present paper details one recently developed adaptive approach which exhibits good computational and convergence properties. This is coupled with a correlation test to show the optimality or nonoptimality of the results in any given application. A seismically excited structure is used to illustrate the required problem formulation and estimation results.     

Application of a genetic algorithm: near optimal estimation of the rate and equilibrium constants of complex reaction mechanisms

In iterative non-linear least-squares fitting, the reliable estimation of initial parameters that lead to convergence to the global optimum can be difficult. Irrespective of the algorithm used, poor parameter estimates can lead to abortive divergence if initial guesses are far from the true values or in rare cases convergence to a local optimum. For determination of the parameters of complex reaction mechanisms, where often little is known about what value these parameters should take, the task of determining good initial estimates can be time consuming and unreliable. In this contribution, the methodology of applying a genetic algorithm (GA) to the task of determining initial parameter estimates that lie near the global optimum is explained. A generalised genetic algorithm was implemented according to the methodology and the results of its application are also given. The parameter estimates obtained were then used as the starting parameters for a gradient search method, which quickly converged to the global optimum. The genetic algorithm was successfully applied to both simulated kinetic measurements where the reaction mechanism contained one equilibrium constant and two rate constants to be fitted, and to kinetic measurements of the complexation of Cu²⁺ by 1,4,8,11-tetraazacyclotetradecane where two equilibrium and two rate constants were fitted. The implementation of the algorithm is such that it can be generally applied to any reaction mechanism that can be expressed by standard chemistry notation. The control parameters of the algorithm can be varied through a simple user interface to account for parameter range and the number of parameters involved.     

The Identification of the Wind Parameters Based on the Interactive Multi-Models

The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. Considering the diversity of the wind, three elemental time-related Markov models with carefully designed parameter α are adopted in the interacting multiple model (IMM) algorithm, to accomplish the estimation of the wind parameters. Furthermore, the estimation accuracy is dependent as well on the filtering technique. In that regard, the sparse grid quadrature Kalman filter (SGQKF) is employed to comprise the computation load and high filtering accuracy. Finally, the proposed algorithm is ran using simulation tests which results demonstrate its effectiveness and superiority in tracking the wind change.     

Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally weighted cubature points, the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function (pdf) to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’ rule. It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system, and thus the importance density function can be used to approximate the true posterior density distribution. In Bayesian filtering, the nonlinear filter performs well when all conditional densities are assumed Gaussian. When applied to the nonlinear/non-Gaussian distribution systems, the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle filter-based approaches, such as the extended particle filter (EPF), and unscented particle filter (UPF), and also the Kalman filter (KF)-type approaches, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF) and CKF. Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.     

一种抑制卡尔曼滤波发散的实时数据处理方法

由于水声环境的复杂性和水声信道的时空变特性及水下航行载体的机动性,水声定位系统测量的弹道样点野值较多,平滑性差。介绍了一种野值的自动剔除和卡尔曼滤波递推处理方法,克服了滤波发散。文中选取距离D的倒数作为状态变量,使得1／D是近似线性变化的,此时量测方程的误差也近似是线性的,卡尔曼滤波器的表现是稳定的,并且是渐近无偏的。卡尔曼滤波的递推形式,滤波增益矩阵Kk的离线计算出,Qk和Rk值选取固定植,野值设定门限自动剔除,使滤波器收敛和稳定时间短,实现了对快速目标的跟踪和滤波输出,没有出现发散现象。该方法的特点是实时性好,对快速目标具有良好的跟踪能力,而且能达到工程上应用的精度要求。     

基于FxKalman算法的有源控制器设计与实现研究

近年来,对于有源噪声控制算法的性能越来越重视。与基于维纳滤波原理的最小均方滤波(Filtered-x Least Mean Square,Fx LMS)、最小二乘滤波(Filtered-x Recursive Least Square,Fx RLS)算法相比较,基于卡尔曼滤波的有源控制算法(Filtered-x Kalman,Fx Kalman)具有较快的收敛速度和良好的跟踪性能,且对带宽噪声有较好的降噪性能。设计、仿真运行了Fx Kalman算法的有源控制器,并针对单频、窄带和宽带信号,在实验室封闭空间对Fx Kalman算法、Fx LMS算法和Fx RLS算法进行有源控制器验证性实验比较,证实了Fx Kalman有源控制器具有上述优点。而如果初级噪声为单频信号且对算法收敛速度要求不高,Fx LMS算法是最经济稳妥的选择。当需要控制带宽噪声或对算法收敛速度要求较高时,Fx Kalman算法则为最好的选择。     

解调分析中差频现象的理论分析及细化解调／频谱分析集成算法

应用泰勒级数展开方法研究了解调分析中的差频现象,指出信号解调前必须滤除与调制信息无关的加性频率成分。为方便实际应用,提出了细化解调／频谱分析集成算法,算法中两种信号分析方法均由带通滤波、Hibert变换和重抽样三个步骤组成,算法实现时将这三个步骤集成在一个复解析带通滤波过程之中,具有很高的计算效率,算法的有效性得到了仿真的验证。