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.     

基于修正积分卡尔曼粒子滤波的自适应目标跟踪算法

针对当前粒子滤波权值退化问题以及精度与时耗的矛盾,提出了一种新的高精度自适应粒子滤波算法。该算法综合考虑优选建议分布函数和重采样两种并行改进滤波性能的方法:首先,在积分卡尔曼滤波(QKF)的基础上引入修正因子,通过修正的积分卡尔曼滤波(PQKF)产生优选的建议分布函数,较好地克服了粒子退化现象,在提高滤波精度的同时降低了运算量;在重采样阶段,通过引入系统估计和预测提供的新息差值在线自适应调整采样粒子数,较好地保证了粒子采样的高效性和算法的实时性。实验表明,新算法具有高精度、低时耗的优点,是一种高精度自适应粒子滤波算法。     

多模噪声中粒子滤波算法研究

粒子滤波是一种解决非高斯滤波问题的有效方法,受到许多领域的研究人员的重视。在扩展卡尔曼滤波(EKF)的基础上,提出一种基于多层感知器(MLP)的扩展卡尔曼滤波算法。利用扩展卡尔曼粒子滤波器和MLP对当前时刻状态重要性采样,引入MLP对样本进行重采样。该算法能有效利用测量值的最新信息,对状态估计的误差更小。在实验中,对于多模噪声非线性系统,该算法与另外算法进行比较。结果证明,所提算法性能优异于其他算法。     

Computing arrival cost parameters in moving horizon estimation using sampling based filters

Moving horizon estimation (MHE) is a numerical optimization based approach to state estimation, where the joint probability density function (pdf) of a finite state trajectory is sought, which is conditioned on a moving horizon of measurements. The joint conditional pdf depends on the a priori state pdf at the start of the horizon, which is a prediction pdf based on historical data outside the horizon. When the joint pdf is maximized, the arrival cost is a penalty term based on the a priori pdf in the MHE objective function. Traditionally, the a priori pdf is assumed as a multivariate Gaussian pdf and the extended Kalman filter (EKF) and smoother are used to recursively update the mean and covariance. However, transformation of moments through nonlinearity is poorly approximated by linearization, which can result in poor initialization of MHE. Sampling based nonlinear filters completely avoid Taylor series approximations of nonlinearities and attempt to approximate the non-Gaussian state pdf using samples and associated weights or probability mass points. The performance gains of sampling based filters over EKF motivate their use to formulate the arrival cost in MHE. The a priori mean and covariance are more effectively propagated through nonlinearities and the resulting arrival cost term can help to keep the horizon small. It is also possible to find closed-form approximations to the non-Gaussian a priori pdf from the sampling based filters. Thus, more realistic nonparametric arrival cost terms can be included by avoiding the Gaussian assumption. In this paper the use of the deterministic sampling based unscented Kalman filter, the class of random sampling based particle filter and the aggregate Markov chain based cell filter are discussed for initializing MHE. Two simulation examples are included to demonstrate the benefits of these methods over the traditional EKF approach.     

基于UKF和优化组合策略的改进粒子滤波算法

针对标准粒子滤波算法存在的粒子退化与贫化问题,提出了一种新的改进粒子滤波算法。该算法采用无迹卡尔曼滤波、优化组合策略和标准粒子滤波相结合的方法,运用UKF产生重要性密度函数,解决标准PF算法中以先验概率密度函数作为建议分布所引发的退化问题;运用优化组合重采样策略保证所有粒子的信息以一定概率得到继承,维持粒子集中粒子的多样性。理论分析与仿真结果均表明,改进算法能有效地解决标准粒子滤波存在的粒子退化问题并避免粒子贫化现象的出现,具有更高的状态估计精度。     

卡尔曼粒子滤波的视频车辆跟踪算法研究

近年来,视频车辆跟踪作为城市智能交通系统（ITS）的一个关键技术受到关注。本文针对传统粒子滤波的非线性、非高斯性可能导致跟踪过程的不稳健性,提出一种基于卡尔曼粒子滤波的视频车辆跟踪算法,该算法利用基于重要区域的目标颜色直方图统计模型对视频车辆目标进行建模,并将其应用于卡尔曼滤波更新中,通过采用Mean Shift算法将卡尔曼滤波器引用到粒子滤波器当中,对车辆的运行轨迹进行校正,实现了局部线性滤波,实现了在保持跟踪系统整体上的非线性、非高斯性的同时,兼顾其局部的线性高斯特性。实验结果表明,本文所提出的方法与传统粒子滤波方法相比,能够更准确地对车辆进行跟踪,同时保证了在复杂环境下性能的稳健性。     

基于逆滤波器的时变MIMO信道跟踪算法

对时变多发多收（MIMO）无线数字信道,在信道接收端应用逆滤波器算法,得到无线MIMO数字系统的信道冲击响应函数,并将此函数作为初始值,应用改进的粒子滤波器算法,完成时变MIMO数字系统的盲信道跟踪。该算法同时利用了信号的时间分集信息和空间分集信息。计算机仿真表明,与仅利用信号空间分集信息的逆滤波器算法相比,该算法在输入信号小信噪比情况下有更好的全局收敛特性;与卡尔曼滤波算法相比,得到的归一化信道误差更小。     

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications, due to the fact that the covariances of noises are not exactly known. Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE) and the true mean square errors(TMSE) become inconsistent, while FMSE and TMSE are consistent in the Kalman filter with accurate models. This can lead to low credibility of state estimation regardless o...     

Hybrid three-dimensional camera pose estimation using particle filter sensor fusion

To properly align objects in the real and virtual worlds in an augmented reality (AR) space it is essential to keep tracking the camera's exact three-dimensional position and orientation (camera pose). State-of-the-art analysis shows that traditional vision-based or inertial sensor-based solutions are not adequate when used individually. Sensor fusion for hybrid tracking has become an active research direction during the past few years, although how to do it in a robust and principled way is still an open problem. In this paper, we develop a hybrid camera pose-tracking system that combines vision and inertial sensor technologies. We propose to use the particle filter framework for the sensor fusion system. Particle filters are sequential Monte-Carlo methods based upon a point mass (or 'particle') representation of probability densities, which can be applied to any state space model and which generalize the traditional Kalman filtering methods. We have tested our algorithm to evaluate its performance and have compared the results obtained by the particle filter with those given by a classical extended Kalman filter. Experimental results are presented     

An Improved Dual Unscented Kalman Filter for State and Parameter Estimation

A dual unscented Kalman filter (DUKF) is used to estimate the state and the parameter simultaneously via two parallel unscented Kalman filters. The original DUKF usually has performance degradation as a result of assuming the control inputs of each filter are constant, which usually are disturbance inputs or systematic measurement errors in the control system. An improved dual unscented Kalman filter (IDUKF) with random control inputs and sequential dual estimation structure is derived and applicable to the system in which the parameter is linearly observed and uncorrelated with the state. The accuracy, observability, and computational efficiency of the new filter are discussed. Then, the expansibility of the IDUKF for nonlinear parameter observed substructures is investigated. Finally, two simulation experiments about space target tracking and typical time series filtering are shown. The theoretical analyses and simulation results demonstrate the following. (1) the IDUKF can obtain higher accuracy than the original DUKF and a comparative accuracy with the JUKF (joint unscented Kalman filter) when the state and the parameter are not strongly correlated; (2) the IDUKF has better applicability than the DUKF when the state is correlated with the unknown parameter; (3) when the modeling error is not ignorable, the IDUKF is more robust and more accurate than the JUKF due to lower sensitivity to the modeling error.     

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。     

考虑星间测量的航天器自主导航并行滤波器

针对基于星间测量的航天器自主导航问题,本文考虑测量中存在野值的情况,提出了一种轨道根数辅助估计的并行无迹卡尔曼滤波算法.系统由两个并行滤波器组成,通过副滤波器的状态估计识别观测野值,进而在主滤波器中修正导航定位结果.文章选择了星间相对观测两卫星编队的基本构型,研究了算法的阈值参数选择,对不同参数条件下的滤波结果进行了对比.数值仿真说明了该算法在观测量变化率较大时能够有效降低连续野值对自主导航系统的影响,和传统算法相比具有更高的滤波精度和收敛速度.     

基于Huber M估计的鲁棒Cubature 卡尔曼滤波算法

Cubature卡尔曼滤波器(CKF)在非高斯噪声或统计特性未知时滤波精度将会下降甚至发散,为此提出了统计回归估计的鲁棒CKF算法.推导出线性化近似回归和直接非线性回归的鲁棒CKF算法,直接非线性回归克服了观测方程线性化近似带来的不足.具有混合高斯噪声的仿真实例比较了3种Cubature卡尔曼滤波器的滤波性能,结果表明这两种鲁棒CKF滤波精度及估计一致性明显优于CKF,直接非线性回归的CKF的鲁棒性更强,滤波性能更好.     

Comparison of stochastic filtering methods for 3D tracking

In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.     

改进型粒子滤波算法在多站纯方位被动跟踪中的应用

针对多站纯方位被动定位与跟踪问题,给出了一种基于均匀重采样和带白适应因子的改进型粒子滤波算法．首先,基于无迹卡尔曼（UKF）粒子滤波器,将参考分布融入最新观测信息,得到符合真实状态的后验概率分布：借助重采样和使用鲁棒估计,改善了粒子滤波的退化问题．其次,引入自适应因子以调整UKF的状态模型协方差与观测模型协方差的比例,得到较高精度的概率分布．仿真结果表明,改进的粒子滤波算法能够实现多站纯方位被动跟踪,比传统非线性滤波器有更高的跟踪精度．     

An evaluation of the nonlinear/non-Gaussian filters for the sequential data assimilation

This paper aims to investigate several new nonlinear/non-Gaussian filters in the context of the sequential data assimilation. The unscented Kalman filter (UKF), the ensemble Kalman filter (EnKF), the sampling importance resampling particle filter (SIR-PF) and the unscented particle filter (UPF) are described in the state-space model framework in the Bayesian filtering background. We first evaluated those methods with a simple highly nonlinear Lorenz model and a scalar nonlinear non-Gaussian model to investigate the filter stability and the error sensitivity, and then their abilities in the one-dimensional estimation of the soil moisture content with the synthetic microwave brightness temperature assimilation experiment in the land surface model VIC-3L. All the results are compared with the EnKF. The advantages and disadvantages of each filter are discussed.The results in the Lorenz model showed that the particle filters are suitable for the large measurement interval assimilation and that the Kalman filters were suitable for the frequent measurement assimilation as well as small measurement uncertainties. The EnKF also showed its feasibility for the non-Gaussian noise. The performance of the SIR-PF was actually not as good as that of the UKF or the EnKF regarding a very small observation noise level compared with the uncertainties in the system. In the one-dimensional brightness temperature assimilation experiment, the UKF, the EnKF and the SIR-PF all proved to be flexible and reliable nonlinear filter algorithms for the low dimensional sequential land data assimilation application. For the high dimensional land surface system that takes the horizontal error correlations into account, the UKF is restricted by its computational demand in the covariance propagation; we must use the EnKF, the SIR-PF and other covariance reduction algorithms. The large computational cost prevents the UPF from being applied in practice.     

Practical issues in state estimation using particle filters: Case studies with polymer reactors

In this paper, we compare Kalman update based filters with particle filters using simulations on polymerization processes. In particular, we compare the unscented Kalman filter (UKF) and the particle filter (PF) for the case of significant plant–model mismatch. The sequential importance resampling particle filter is shown to be less robust than the Kalman update-based filters. This issue is solved by bootstrapping the PF with the UKF, i.e., using the UKF as the proposal distribution; this retains its ability to estimate non-Gaussian distributions while providing robustness with respect to plant–model mismatch. Finally, we explore methods of obtaining a point estimate from the state distributions of the PF.     

On the choice of importance distributions for unconstrained and constrained state estimation using particle filter

Recursive state estimation of constrained nonlinear dynamical system has attracted the attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation problems, particle filters have been widely used (Arulampalam et al. [1]). As pointed out by Daum [2], particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. In this paper, a novel approach for generating the proposal distribution based on a constrained Extended Kalman filter (C-EKF), Constrained Unscented Kalman filter (C-UKF) and constrained Ensemble Kalman filter (C-EnkF) has been proposed. The efficacy of the proposed state estimation algorithms using a particle filter is illustrated via a successful implementation on a simulated gas-phase reactor, involving constraints on estimated state variables and another example problem, which involves constraints on the process noise (Rao et al. [10]). We also propose a state estimation scheme for estimating state variables in an autonomous hybrid system using particle filter with Unscented Kalman filter as a proposal and unconstrained Ensemble Kalman filter (EnKF) as a proposal. The efficacy of the proposed state estimation scheme for an autonomous hybrid system is demonstrated by conducting simulation studies on a three-tank hybrid system. The simulation studies underline the crucial role played by the choice of proposal distribution in formulation of particle filters.     

非高斯噪声中的粒子滤波算法研究

在非线性非高斯动态系统中,粒子滤波已成为解决系统参数估计和状态滤波的主流方法。然而,粒子退化是粒子滤波中不可避免的现象,粒子重采样是解决方法之一。本文针对粒子退化现象,在扩展卡尔曼滤波器的基础上研究了一种基于支持向量机粒子滤波算法,算法实现中扩展卡尔曼粒子滤波器结合支持向量机对当前时刻的重要性采样,再对粒子样本进行重采样。该算法能有效地利用量测值的最新信息,状态估计误差较小,同时避免了粒子匮乏。理论分析和仿真结果表明,新算法在双模噪声非线性系统估计的精度优于标准粒子滤波算法与扩展卡尔曼粒子滤波算法。     

稳态Kalman滤波器增益新算法

应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增 益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器 伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.