基于自适应卡尔曼滤波的运动车辆检测

卡尔曼滤波是一种根据时变随机信号的统计特性,对信号的未来值做出尽可能接近真值的一种估计方法,首先介绍了卡尔曼滤波原理,然后阐述了它在运动目标检测的应用。针对传统的固定值的卡尔曼滤波方法的缺陷,提出了自适应更新参数的卡尔曼滤波方法。通过与传统的卡尔曼滤波方法、帧差法、光流法和高斯混合模型方法的比较,证明了该方法的有效性。     

无模型容积卡尔曼滤波及其应用

提出一种融合高斯过程回归(GPR)的无模型容积卡尔曼滤波(MF-CKF)方法.容积卡尔曼滤波(CKF)是一种新的非线性高斯滤波方法,比无迹卡尔曼滤波(UKF)更具优势.为了克服建模不准确时容积卡尔曼滤波精度下降问题,通过将高斯过程回归引入到容积卡尔曼滤波之中,对训练数据学习建立系统非线性模型,从而有效地避免模型不准确造成的滤波性能下降.仿真结果验证了无模型容积卡尔曼滤波在系统模型不准确情况下的优越性.     

改进的自适应衰减卡尔曼滤波算法

SINS/GPS组合导航系统的融合算法主要是卡尔曼滤波,卡尔曼滤波实现最优估计的前提是系统的模型和随机噪声信息必须准确已知。实际情况下,大部分系统的模型和随机噪声信息不完全可知,这可能会导致滤波器估计精度下降。针对这一问题,根据求解遗传因子的方法不同对传统的自适应衰减卡尔曼滤波进行改进,提出一种改进的自适应衰减卡尔曼滤波。改进后的算法分别适用于系统噪声统计模型不准确可知和量测噪声统计模型不准确可知两种情况,分别对应于两种滤波算法,并且二者具有统一的滤波框架。仿真结果表明,改进的自适应衰减卡尔曼滤波比卡尔曼滤波精度较高,有效解决了因为噪声模型不准确导致的精度下降问题。     

基于卡尔曼滤波的移动节点定位研究

基于RSSI(received signal strength indication)的定位方法由于成本低廉而得到了广泛的应用,但其容易受到环境因素的影响使得定位精度下降。针对这个问题,提出了一种基于卡尔曼滤波的移动节点定位方法。简要介绍了卡尔曼滤波技术,在RSSI定位的基础上,建立了移动节点的运动模型,对所建模型进行了滤波稳定性的分析。采用Matlab软件对运动模型进行卡尔曼滤波仿真,结果表明,卡尔曼滤波可以有效地对移动节点跟踪定位。     

基于变维交互作用的IMM-CKF算法

为了对非线性情况下的机动目标进行跟踪,提出一种变维交互作用的交互多模型容积卡尔曼滤波(IMM-CKF)算法。该算法结合变维交互作用多模型滤波方法和容积卡尔曼滤波方法,对使用相同维数模型的IMM-CKF算法进行扩展,使其能够应用于更一般的目标跟踪环境。仿真结果表明,该算法的跟踪精度明显高于变维交互作用下的交互多模型不敏卡尔曼滤波(IMM-UKF)算法,验证了该算法的可行性和有效性。     

一种改进扩展卡尔曼滤波新方法

针对现有的迭代扩展卡尔曼滤波（EIEKF）跟踪时估计精度较低这一不足,提出了一种改进扩展卡尔曼滤波（NIEKF）新方法。本文将迭代滤波理论引入到扩展卡尔曼滤波方法中,重复利用观测信息,采用经典的非线性非高斯模型进行仿真实验,给出了该方法与扩展卡尔曼滤波（EKF）、Unscented 卡尔曼滤波（UKF）、现有的迭代扩展卡尔曼滤波（EIEKF）的仿真结果,并分析了其跟踪性能和均方根误差。实验结果表明,改进扩展卡尔曼滤波（NIEKF）新方法具有更高的估计精度。     

卡尔曼滤波技术在捷联惯性导航系统对准中的应用

本文首先阐述了卡尔曼滤波技术的简单理论,然后建立了捷联惯性导航系统初始对准的卡尔曼滤波模型,并对静基座下的卡尔曼滤波初始对准精度进行分析,最后进行了卡尔曼滤波仿真。仿真结果表明,此方法算法简单,能有效缩短初始对准时间,对准稳态精度较高,是一种可靠的初始对准方案。     

非线性动态滤波的迭代算法

阐述了标称状态的线性化方法和扩展的卡尔曼滤波公式及迭代卡尔曼滤波，探讨了非线性动态滤波的近似处理方法，围绕标称状态将非线性模型进行线性化，将标准的卡尔曼滤波扩展到非线性模型，得到扩展的卡尔曼滤波公式，研究了迭代滤波计算方法。扩展的卡尔曼滤波方法已经有效地用于非线性模型。     

基于改进卡尔曼滤波的RFID数据清洗方法研究

采用RFID(radio frequency identification)技术在阅读器和动态电子标签之间进行通信,采集到的数据通常存在大量的脏数据。为更好支持高层应用,必须对原始数据进行清洗。针对标签频繁移动这一特点,将滑动窗口技术引入卡尔曼滤波模型,给出了一种改进的卡尔曼滤波模型,进而提出了一种基于改进卡尔曼滤波的RFID数据清洗方法。该方法在保证数据清洗准确率的基础上能有效解决标签动态跃迁带来的时间延迟问题,从而更加适用于标签频繁移动的场景。实验结果表明,该算法提高了清洗效率及准确率。     

基于卡尔曼滤波算法的电能质量检测技术实现

为提高电能质量的检测精度，提出一种基于自适应卡尔曼滤波的检测方法。首先，构建基于卡尔曼滤波的空间模型，然后引入自适应卡尔曼滤波算法对空间模型中参数Q_k进行修正；最后搭建DSP仿真平台对空间模型进行训练与测试。结果表明：相比于传统的卡尔曼滤波算法，基于自适应卡尔曼滤波算法对电能质量扰动信号的敏感性更好，响应速度更快，精准度更高。由此得出，自适应卡尔曼滤波算法在电能检测方面性能更佳。     

基于无极卡尔曼滤波算法的雅可比矩阵估计

在基于图像的机器人视觉伺服中,采用在线估计图像雅可比的方法,不需事先知道系统的精确模型,可以避免复杂的系统标定过程。为了有效改善图像雅可比矩阵的在线估计精度,进而提高机器人的跟踪精度,针对机器人跟踪运动目标的应用背景,提出了利用无极卡尔曼滤波算法在线估计总雅可比矩阵。在二自由度的机器人视觉伺服仿真平台上,分别用卡尔曼滤波器（KF）、粒子滤波器（PF）和无极卡尔曼滤波器（UKF）三种算法进行总雅可比矩阵的在线估计。实验结果证明,使用UKF算法的跟踪精度优于其他两种算法,时间耗费仅次于KF算法。     

A chaotic communication system of improved performance based on the Derivative-free nonlinear Kalman filter

The Derivative-free nonlinear Kalman Filter is used for developing a communication system that is based on a chaotic modulator such as the Duffing system. In the transmitter’s side, the source of information undergoes modulation (encryption) in which a chaotic signal generated by the Duffing system is the carrier. The modulated signal is transmitted through a communication channel and at the receiver’s side demodulation takes place, after exploiting the estimation provided about the state vector of the chaotic oscillator by the Derivative-free nonlinear Kalman Filter. Evaluation tests confirm that the proposed filtering method has improved performance over the Extended Kalman Filter and reduces significantly the rate of transmission errors. Moreover, it is shown that the proposed Derivative-free nonlinear Kalman Filter can work within a dual Kalman Filtering scheme, for performing simultaneously transmitter–receiver synchronisation and estimation of unknown coefficients of the communication channel.     

Sparse-grid quadrature nonlinear filtering

In this paper, a novel nonlinear filter named Sparse-grid Quadrature Filter (SGQF) is proposed. The filter utilizes weighted sparse-grid quadrature points to approximate the multi-dimensional integrals in the nonlinear Bayesian estimation algorithm. The locations and weights of the univariate quadrature points with a range of accuracy levels are determined by the moment matching method. Then the univariate quadrature point sets are extended to form a multi-dimensional grid using the sparse-grid theory. Compared with the conventional point-based methods, the estimation accuracy level of the SGQF can be flexibly controlled and the number of sparse-grid quadrature points for the SGQF is a polynomial of the dimension of the system, which alleviates the curse of dimensionality for high dimensional problems. The Unscented Kalman Filter (UKF) is proven to be a subset of the SGQF at the level-2 accuracy. The performance of this filter is demonstrated by an orbit estimation problem. The simulation results show that the SGQF achieves higher accuracy than the Extended Kalman Filter (EKF), the UKF, and the Cubature Kalman Filter (CKF). In addition, the SGQF is computationally much more efficient than the multi-dimensional Gauss–Hermite Quadrature Filter (GHQF) with the same performance.     

Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.     

基于UKF的CDMA系统信道参数估计

CDMA系统信道时间延迟估计是一个非线性的迭代过程。UKF算法能够避免EKF由于线性化非线性系统而带来的误差过大等问题,比EKF估计的更加精确。利用UKF算法对CDMA系统信道的幅度衰减参数与延时参数进行了估计。在研究中考虑到了多址干扰和远近效应对信道参数的影响,仿真结果表明UKF算法能有效地抑制远近效应及多址干扰,估计出无线信道参数。     

Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

An underwater vehicle is useful in the monitoring of the unstructured and dangerous underwater conditions. One of the unmanned underwater vehicle is AUV. AUV is a robotic device that is driven through the water by a propulsion system, controlled and piloted by an onboard computer, and maneuverable in three dimensions. This research explains about position estimation of AUV based on the Ensemble Kalman Filter (EnKF) and the Fuzzy Kalman Filter (FKF). EnKF is used as the estimation method of AUV’s position that maneuvering in 6 DOF (Degrees of Freedom) with the specified trajectory. The estimation results are simulated with Matlab. The simulations show the AUV position estimation based on the EnKF with some of the different ensembles and the comparison results of the position estimation between the EnKF and the FKF. The final result of these study shows that Ensemble Kalman Filter is better to estimate the trajectory of the dynamical equation of AUV motion with the error estimation of EnKF is 92% smaller in the x-position dan y-position, 6.5% smaller in the z-position, 93% smaller in the angle dan the computation of time is 50% faster than the estimation results of FKF.     

基于CDKF的飞机姿态角估计

针对扩展卡尔曼滤波(Extend Kalman Filter ,EKF)在飞机姿态估计中存在着计算复杂、线性化误差大等缺点,将一种基于Stirling内插公式的非线性滤波算法—中心差分卡尔曼滤波算法(Central Difference Kalman Filter, CDKF)应用于由低精度高噪声传感器组成的低成本飞机姿态估计系统中。首先建立基于四元数的飞机姿态数学模型,然后用CDKF方法进行姿态估计,并通过实测数据进行验证。实验结果表明, CDKF方法不仅有效地提高了飞机姿态估计的精度和稳定性;而且不需要模型的具体解析形式,避免了复杂的Jacobian矩阵的计算,算法更简单,也更容易实现,优于常用的EKF方法。     

带非线性等式约束无迹卡尔曼滤波方法

针对带非线性等式约束的非线性系统的状态估计问题,给出了一种新形式的基于无迹卡尔曼滤波及伪观测手段的处理约束的状态估计方法（SPUKF）。在该方法中原动态系统被虚拟地分离成两个并行的子系统,各时刻的状态估计由基于这两个子系统构建的两套滤波链交替得到。相对于伪观测法中的序贯形式估计器,SPUKF无需事先确定观测及约束的处理次序且能获得更好的估计结果,故可以用来解决序贯方法中观测与约束的处理次序问题。由钟摆运动的实例仿真结果看到,SPUKF不仅有好于序贯形式无迹卡尔曼滤波的估计效果,误差改善比达到22%左右,而且算法运行时间与序贯形式估计器相近。此外,其估计效果还与批处理无迹卡尔曼滤波相当。     

基于加权最小二乘的卡尔曼滤波算法

为了将卡尔曼滤波(KF)应用于非线性系统中,利用了离散采样点将非线性模型线性化.通过加权最小二乘原理.得到近似的线性化模型,再将KF算法应用于这个线性模型中.结果表明,加权最小二乘与KF结合的方法在非线性模型中的计算结果同扩展卡尔曼滤波(EKF)算法接近,且不需要EKF那样求偏导就能很容易地应用到非线性系统中.这种方法实现容易,预测可靠,具有实际应用的价值.