基于UKF的高斯和滤波算法

介绍了扩展卡尔曼滤波算法和无迹变换（unscented transformation,UT）算法,并对扩展卡尔曼滤波算法（EKF）和无迹卡尔曼滤波算法（UKF）进行比较,阐明了UKF优于EKF。在此基础上,提出了一种基于Unscented变换（UT）的高斯和滤波算法,该算法首先通过合并准则得到适当个数的混合高斯模型,逼近系统中非高斯噪声的概率密度;然后,再通过UT算法进行滤波。最后分别对基于EKF和UKF的滤波方法进行实验,并对实验结果进行比较与分析,验证了算法的有效性和优良性。     

基于高斯和SCKF 的姿态角辅助三维目标跟踪

首先, 根据目标运动与姿态角的关系, 分析目标在偏航角和俯仰角下的速度变化, 进而推导出姿态角辅助三维目标跟踪模型; 然后, 针对姿态角量测非高斯情况, 在分析均方根容积卡尔曼滤波的基础上, 提出新的高斯和均方根容积卡尔曼滤波算法, 以提高非线性非高斯的处理能力; 最后, 结合不同运动模式下姿态角分量的特点, 建立姿态角分量不同的跟踪模型, 通过模型切换实现对姿态角机动的跟踪. 仿真结果验证了所提出跟踪模型和滤波算法的正确性和有效性.     

基于均差滤波与高斯和的非线性非高斯系统滤波算法

针对一类非线性非高斯系统的滤波问题,在分析均差滤波算法和高斯和滤波算法的基础上,提出一种基于均差滤波的高斯和滤波算法,适于处理非线性非高斯系统的滤波问题.对于似然密度位于条件转移概率密度拖尾处的情况,与传统的粒子滤波算法相比,所提算法能提高滤波的精度和实时性.仿真实验验证了新算法的有效性.     

基于SRCKF 的自适应高斯和状态滤波算法

针对非线性非高斯离散动态系统中的状态估计问题,基于高斯和递推关系,提出一种高斯和状态估计算法GSSRCKF.首先将状态噪声、观测噪声及滤波初值均表示为高斯和的形式,以平方根容积卡尔曼滤波为子滤波器分别估计各高斯子项对应的系统状态;然后结合各子项对应的权值实现全局估计;最后设计高斯子项对应权值的自适应策略,并采用约简控制法降低计算复杂度.仿真结果验证了所提出的算法在滤波稳定性方面的优越性.     

基于粒子滤波的机动目标跟踪算法仿真研究

针对非线性多目标模型,应用粒子滤波算法,这种方法不受模型线性和Gauss假设的约束,是一种处理非线性非高斯动态系统状态递推估计的有效算法。在粒子滤波的基础上融合扩展卡尔曼滤波算法和无迹卡尔曼滤波算法。融合后的新算法在计算提议概率密度分布时,粒子的产生充分考虑当前时刻的量测,使得粒子的分布更加接近状态的后验概率分布,再用平滑算法处理滤波的结果。仿真结果表明,算法有较好的跟踪效果。     

基于KLD采样的自适应粒子滤波目标跟踪算法

标准粒子滤波算法用于无线传感器网络运动目标跟踪时,非高斯噪声环境会降低其跟踪精度和计算效率。针对该问题,结合多传感器测量模型和Kullback-Leibler距离(KLD)采样方法,提出一种自适应粒子滤波算法。在满足预设阈值条件时,引入补偿函数对重要性概率密度函数(IPDF)进行迭代更新,同时利用具有自适应退火参数的模拟退火算法使粒子快速接近高似然区域。在此基础上,结合KLD采样动态调整粒子规模,在保证跟踪精度的同时减少运算量。仿真结果表明,与KLD-PF算法相比,该算法的IPDF分布接近真实后验概率密度分布,跟踪精度较高,能够在不同参数的非高斯噪声下进行有效跟踪。     

非线性滤波算法在机动目标跟踪中的研究

介绍了3种最基本非线性滤波算法--扩展卡尔曼滤波(EKF)、无迹卡尔曼滤波(UKF)和粒子滤波(PF)算法的理论在机动目标跟踪中的应用.通过仿真试验对三者性能进行了分析比较.     

基于目标尺度的自适应高斯滤波

将具有形态学意义的目标尺度与传统的线性高斯滤波相结合提出了一种自适应高斯滤波,它的主要思想是：利用求出的目标尺度来控制各像素点高斯滤波的方差和模板大小。针对原有的目标尺度求解算法不能适应可见光图像去噪的问题,一方面,引入中值滤波以去除对目标尺度求解影响较大的强噪声点,另一方面,又调整了求解目标尺度算法中的参数。仿真实验证明,该算法可以在去除噪声的同时保护图像的细节,而且不论从主观上还是客观上都优于传统的几类图像平滑算法,且不需要迭代求解,计算简单。     

基于高斯粒子滤波法的CIR利率期限结构估计

为了提高CIR利率期限结构模型中的状态估计精度,建立了该问题的离散非线性滤波模型,采用高斯粒子滤波法进行状态近似最优估计.相对于文献中普遍采用的扩展卡尔曼滤波方法,高斯粒子滤波法避免了线性近似带来的误差,利用基于重要性采样得到的高斯分布来近似状态变量的后验分布,具有更强的状态估计能力.仿真实验比较了高斯粒子滤波和扩展卡...     

一种用于解决非线性滤波问题的新型粒子滤波算法

粒子滤波算法受到许多领域的研究人员的重视,该算法的主要思想是使用一个带有权值的粒子集合来表示系统的后验概率密度.在扩展卡尔曼滤波和Unscented卡尔曼滤波算法的基础上,该文提出一种新型粒子滤波算法.首先用Unscented卡尔曼滤波器产生系统的状态估计,然后用扩展卡尔曼滤波器重复这一过程并产生系统在k时刻的最终状态估计.在实验中,针对非线性程度不同的两种系统,分别采用5种粒子滤波算法进行实验.结果证明,文中所提出的算法的各方面性能都明显优于其他4种粒子滤波算法.     

Robust Kalman filtering for nonlinear multivariable stochastic systems in the presence of non‐Gaussian noise

The presence of outliers can considerably degrade the performance of linear recursive algorithms based on the assumptions that measurements have a Gaussian distribution. Namely, in measurements there are rare, inconsistent observations with the largest part of population of observations (outliers). Therefore, synthesis of robust algorithms is of primary interest. The Masreliez–Martin filter is used as a natural frame for realization of the state estimation algorithm of linear systems. Improvement of performances and practical values of the Masreliez‐Martin filter as well as the tendency to expand its application to nonlinear systems represent motives to design the modified extended Masreliez–Martin filter. The behaviour of the new approach to nonlinear filtering, in the case when measurements have non‐Gaussian distributions, is illustrated by intensive simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

自适应高斯粒子滤波在导航中的应用

针对粒子滤波中重要性密度函数难以选取和粒子退化导致的计算精度下降的问题,提出一种新的自适应高斯粒子滤波算法。通过高斯混合密度函数和UT变换来获取状态均值和协方差阵,选择并计算合适的自适应因子来调节均值和方差,在迭代过程中可动态调节重要性密度函数,并用WEM和EM步骤代替重采样,上述滤波算法考虑了最新量测信息的影响,使滤波性能明显改善,能更好地解决非线性非高斯系统模型的抗干扰问题。将提出的算法应用于SINS/GPS组合导航系统跑车试验中,结果表明上述滤波算法能提高导航解算的精度,其性能明显优于已有滤波,同时验证了当系统出现噪声干扰突然变化时提出算法的有效性。     

Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings

In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously, with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms.      

Major Development Under Gaussian Filtering Since Unscented Kalman Filter

Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements. Such problems appear in several branches of science and technology, ranging from target tracking to biomedical monitoring. A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering. The early Gaussian filters used a derivative-based implementation, and suffered from several drawbacks, such as the smoothness requirements of system models and poor stability. A derivative-free numerical approximation-based Gaussian filter, named the unscented Kalman filter (UKF), was introduced in the nineties, which offered several advantages over the derivative-based Gaussian filters. Since the proposition of UKF, derivative-free Gaussian filtering has been a highly active research area. This paper reviews significant developments made under Gaussian filtering since the proposition of UKF. The review is particularly focused on three categories of developments: i) advancing the numerical approximation methods; ii) modifying the conventional Gaussian approach to further improve the filtering performance; and iii) constrained filtering to address the problem of discrete-time formulation of process dynamics. This review highlights the computational aspect of recent developments in all three categories. The performance of various filters are analyzed by simulating them with real-life target tracking problems.      

Nonlinear Filtering of Non-Gaussian Noise

This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.     

基于高斯似然近似的自适应球面径向积分滤波算法

针对量测噪声较小的环境下传统滤波算法容易出现偏差增大的实际问题, 基于高斯近似原理, 提出一种基于高斯似然近似的球面径向积分滤波(SRGLAF) 算法. 为进一步解决量测未知环境下的状态估计问题, 充分结合CKF 等确定性采样型滤波算法和SRGLAF 的优势, 设计一种基于高斯似然近似的自适应球面径向积分滤波(ASRGLAF) 算法. 仿真结果表明: SRGLAF 能够提高量测噪声较小环境下的估计精度, 而在量测噪声未知环境中, ASRGLAF 能够有效地进行状态估计, 具有明显的滤波优势.

     

多率系统Kalman滤波算法的鲁棒性分析

多率系统Kalman滤波算法是多率采样系统中对多源观测进行融合的重要手段. 基于化工过程的多率采样特点, 给出了多率Kalman滤波算法, 分析了该算法在模型失配情况下的鲁棒性. 在给定的假设条件下, 通过对滤波误差变化规律的分析, 给出了多率Kalman滤波稳定与发散的基本判定方法. 针对一类典型系统, 推导出了滤波稳定与发散判据. 通过仿真对该判据进行了验证, 仿真结果表明所提出的滤波鲁棒性分析方法可以用于算法的实际应用.