几种非线性滤波算法的性能分析

非线性随机动态系统的滤波问题是一类经常遇到的实际应用问题,本文分析了扩展卡尔曼(EKF)、无迹卡尔曼滤波(UKF)和粒子滤波(PF)这三种非线性滤波算法的基本原理和特点以及适应的条件。并通过一个强非线性系统的实验仿真,验证了各自算法的性能。     

基于禁忌遗传粒子滤波的无线传感器网络的目标跟踪

提出了一种禁忌遗传粒子滤波跟踪算法。用遗传算法作全局搜索,用禁忌搜索算法作局部搜索,可以提高遗传算法的局部搜索能力,避免收敛到局部最优点。仿真结果表明:与原算法相比,禁忌遗传粒子滤波算法在大噪声条件下改善了粒子贫乏问题,提高了跟踪精度。     

基于卡尔曼预测的运动目标实时跟踪

针对运动目标实时跟踪问题,采用图像坐标系下的卡尔曼滤波预测来指导跟踪,对被跟踪目标的运动参数(位移,速度)进行滤波预测。并利用Matlab的视频处理功能对该算法进行了计算机仿真。     

非线性互补问题计算机实现

分析了非线性互补问题求解困难，利用粒子群算法并结合极大熵函数法给出了该类问题的一种新的有效算法。该算法首先利用极大熵函数将非线性互补问题转化为一个无约束最优化问题，然后应用粒子群算法来优化该问题，计算机程序实现表明该算法是有效的。     

基于禁忌递阶遗传粒子滤波的无线传感器网络的目标跟踪

提出了一种禁忌递阶遗传粒子滤波跟踪算法.结合禁忌搜索算法和递阶遗传算法提出一种禁忌递阶遗传算法,用递阶遗传算法作全局搜索,用禁忌搜索算法作局部搜索,该算法能在一定程度上克服早熟问题,避免收敛到局部最优点.仿真结果表明:该算法在大噪声条件下改善了粒子贫乏问题,提高了跟踪精度及速度.     

卡尔曼滤波在电池状态预估中的鲁棒性分析

卡尔曼滤波器是基于状态空间模型的最小方差估计,广泛应用于动力电池状态估计领域。而在实际运用中,运用卡尔曼滤波a算法对动力电池状态进行估算的结果通常会出现发散的现象。为了解决这一问题,文章从算法的发散根源出发,根据不同的发散因子,提出相应的改进措施,确保卡尔曼滤波的鲁棒性,并以扩展卡尔曼滤波估算动力电池的电荷状态为例,通过算法改进前后的结果对比,验证了改进算法的有效性,同时也为无迹卡尔曼滤波、中心差分滤波、高斯埃尔米特滤波等相关算法的鲁棒性改进提供了理论指导与参考。     

基于结构光视觉的螺旋焊缝跟踪系统研究

目前,螺旋板式换热器焊接存在焊缝跟踪困难,焊接所导致的偏差存在的缺陷。本文设计和研发了一套基于主动光视觉技术的螺旋焊缝实时跟踪焊接系统,通过焊接前的焊缝特征点提取,焊接时核相关滤波目标跟踪算法进行焊缝的识别和自动跟踪,实验测试表明该方法能够达到螺旋板式换热器的全自动焊接跟踪精度要求。     

卡尔曼滤波在路面检测信号处理中的应用

卡尔曼滤波是一种应用相当广泛的滤波方法。文章将卡尔曼滤波算法用于路面垂直载荷信号处理,通过数学建模、理论分析及实验验证,得到经过卡尔曼滤波算法处理后的路面垂直载荷,提高了数据的可靠性与真实性,对路面检测信号处理研究及采集人员分析路面状况具有重要意义。     

风廓线雷达数据处理EKF方法仿真研究

文章针对风廓线雷达实际工程应用中,处理风场数据时的非线性问题,在传统卡尔曼滤波技术的基础上,提出了基于扩展卡尔曼滤波的数据处理方法。选取2011年的风场数据进行计算机仿真分析,结果表明,该方法可以有效去除风场数据中掺杂的噪声干扰,较好的平滑了未滤波曲线,并弥补了经典卡尔曼滤波参数设置方面的不足,滤波效果优于传统的卡尔曼滤波,具有一定的工程应用前景。     

基于粒子群算法的模糊插值建模方法研究

由于传统模糊建模方法中模型参数都是人们根据经验选取的,它对于不同系统的动态跟踪能力不同,泛化能力差。针对常规模糊推理的局限性,本文提出一种基于参数的模糊插值建模方法。设计了模糊糊理系统的模型和参数调整方案,利用粒子群算法优化模型中的参数,仿真结果表明该方法的有效性。     

基于滤波器的管道突变泄漏检测与定位

在利用卡尔曼滤波新息序列进行泄漏检测的同时,引入强跟踪滤波器性能评价指标,对信号进行变点定位。管道压力信号经卡尔曼滤波后产生新息序列,结合序贯似然比检测(SPRT)检测新息序列,实现管道泄漏检测。强跟踪滤波(STF)跟踪平稳信号时,新息序列满足相互正交,发生泄漏突变后,产生跟踪误差,新息序列相互正交性被破坏。建立新息序列正交性评价指标,作为泄漏的突变点指示,同时实现滤波器工作性能监测。     

改进的卡尔曼滤波算法在行驶汽车状态估计中的应用现状

卡尔曼滤波及其改进方法在行驶车辆状态估计中有着广泛的应用,并取得良好的效果;本文针对自适应卡尔曼滤波算法、无轨迹卡尔曼滤波算法的应用等进行了综合性阐述。     

Applying Kalman Filter on Solving Simultaneous Equations with Overidentifying Rank Restrictions: The Analysis of the Demand and Supply Model of Medium-size Scooter Market in Taiwan

Once the structure form of demand and supply is translated into areduced form, one can solve the reduced form with a state space modelof the Kalman filter method. This paper discusses an innovationrepresentation that links the structure form with the state space model.For the state space model, the recursive Expectation Maximization(EM) algorithm is used to estimate the parameters of a structure form.This research successfully applied the Kalman filter method to theestimation of the coefficients of simultaneous equations withoveridentifying rank restrictions. The empirical monthly data set camefrom the medium-size scooter market in Taiwan during 1987 to 1992period.     

小型无人机导航系统的探索与研究

针对执行特定海域巡航的小型无人机,提出了GPS/INS组合导航系统的解决方案。首先介绍导航系统的算法和原理,根据任务需求,给出硬件构成及软件核心程序流程,通过仿真,证明在组合系统中采取PID控制以及卡尔曼滤波算法大大提高了导航的精度。     

The discrete Kalman filter applied to linear regression models: statistical considerations and an application

In this paper we show how the Kalman filter, which is a recursive estimation procedure, can be applied to the standard linear regression model. The resulting "Kalman estimator" is compared with the classical least-squares estimator.
The applicability and (dis)advantages of the filter are illustrated by means of a case study which consists of two parts. In the first part we apply the filter to a regression model with constant parameters and in the second part the filter is applied to a regression model with time-varying stochastic parameters. The prediction-powers of various "Kalman predictors" are compared with "least-squares predictors" by using T heil 's prediction-error coefficient U.     

Forecasting With the Standardized Self‐Perturbed Kalman Filter

We propose and study the finite‐sample properties of a modified version of the self‐perturbed Kalman filter of Park and Jun (Electronics Letters 1992; 28 : 558–559) for the online estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the amount of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other online algorithms and to classical and Bayesian methods. The standardized self‐perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determines the extent to which realized variance can be used to predict excess returns. Copyright © 2016 John Wiley & Sons, Ltd.     

利用改进的卡尔曼滤波器预测退化设备可靠性

针对退化设备可靠性预测的问题,采用加入次优渐消因子的卡尔曼滤波器预测设备未来时刻退化量均值,进而预测未来时刻的可靠性。本文给出了退化设备的可靠性预测方法和具体算法,这可为确定停机维护时间、维修费用等提供依据,具有重要意义。仿真实验验证了该方法的有效性。     

Nonlinear continuous time modeling approaches in panel research

Stochastic differential equations (SDE) are used as dynamical models for cross-sectional discrete time measurements (panel data). Thus causal effects are formulated on a fundamental infinitesimal time scale. Cumulated causal effects over the measurement interval can be expressed in terms of fundamental effects which are independent of the chosen sampling intervals (e.g. weekly, monthly, annually). The nonlinear continuous–discrete filter is the key tool in deriving a recursive sequence of time and measurement updates. Several approximation methods including the extended Kalman filter (EKF), higher order nonlinear filters (HNF), the local linearization filter (LLF), the unscented Kalman filter (UKF), the Gauss–Hermite filter (GHF) and generalizations (GGHF), as well as simulated filters (functional integral filter FIF) are compared.     

供应链中牛鞭效应的模型与分析

对研究牛鞭效应时经常讨论到的三种模型:系统动力学模型、自回归分析的AR(1)模型及Kalman滤波器(KF)模型进行了分析和比较。