迭代无味卡尔曼滤波器

通过对无味卡尔曼滤波器(Unscented Kalman filter,UKF)的误差进行分析,提出了迭代UKF(IUKF)算法.该基本思路是用测量更新后的状态估计去重新对状态量和观测量的一步预测,然后再次应用LMMSE估计子估计状态量的均值和协方差阵,如此多次迭代后的滤波估计输出具有更高的精度和更小的方差,故滤波器表现出更好的一致性.Monte Carlo仿真表明,IUKF主要应用于观测噪声较小的场合,其中的迭代只需进行2～3次即可.     

A recursive maximum a posteriori estimator

Kalman Filter (KF) is the optimal state estimator for linear dynamical systems in the presence of zero mean white Gaussian noise. It is a minimum mean square error (MMSE) estimator. In the present work a recursive maximum a posteriori estimator (RMAPE) has been developed from basic principles of estimation. This estimator may be used for realtime state estimation of linear dynamical systems in presence of zero mean white Gaussian noise. It is further shown here that the KF can be derived from this RMAPE algorithm, i.e. this work shows an alternative method way to derive the KF. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Suboptimal filter for continuous‐time linear systems with unknown parameters

The filtering problem for continuous‐time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean‐square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real‐time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

中心差分卡尔曼平滑器

针对一类非线性离散系统的状态平滑问题, 本文设计了一种中心差分卡尔曼平滑器(CDKS). 文中基于最小方差估计准则, 详细推导了非线性系统的状态最优平滑递推公式, 并采用中心差分变换来近似计算状态的后验均值和协方差. 相比于传统中心差分卡尔曼滤波器(CDKF), 所设计的CDKS算法有效提高了非线性状态的估计精度, 拓展了中心差分变换的应用范围. 仿真实例验证了所提出平滑器的可行性和有效性.     

New minimum sigma set for unscented filtering

In this work, a new minimum set of sigma points for unscented filtering is proposed along with its unscented Kalman filter in both square‐root and nonsquare‐root forms. Comparative with the other reduced sigma sets of the literature, the new sigma set is, in some cases, better defined or, in another case, a generalization. In numerical examples, the unscented transform of the new sigma set is compared with the unscented transforms of the other reduced sigma sets of the literature. In addition, the performance of the new unscented Kalman filter is studied in an aircraft target tracking scenario. Copyright © 2014 John Wiley & Sons, Ltd.     

多径CD3S信号的多滤波器联合估计解调

针对多径衰落信道下混沌直接序列扩频（CD3S）信号的解调问题,提出一种基于卡尔曼滤波（KF）、无损卡尔曼滤波（UKF）与最小均方误差（MMSE）滤波联合估计的多径CD3S信号解调算法。算法根据混沌码同步、信道估计与信息码解调间的差异性与关联性,利用UKF估计混沌码、KF估计信道参数,并通过MMSE滤波重构信息码。三个滤波器均将彼此的估计结果作为系统参数,交替工作,通过联合估计实现信息码的解调。仿真结果表明,无论对时不变信道还是时变信道,算法均可以克服多径衰落与信道噪声的影响,实现了多径信道下CD3S信号的解调。     

On the optimality of two‐stage Kalman filtering for systems with unknown inputs

This paper is concerned with the optimal solution of two‐stage Kalman filtering for linear discrete‐time stochastic time‐varying systems with unknown inputs affecting both the system state and the outputs. By means of a newly‐presented modified unbiased minimum‐variance filter (MUMVF), which appears to be the optimal solution to the addressed problem, the optimality of two‐stage Kalman filtering for systems with unknown inputs is defined and explored. Two extended versions of the previously proposed robust two‐stage Kalman filter (RTSKF), augmented‐unknown‐input RTSKF (ARTSKF) and decoupled‐unknown‐input RTSKF (DRTSKF), are presented to solve the general unknown input filtering problem. It is shown that under less restricted conditions, the proposed ARTSKF and DRTSKF are equivalent to the corresponding MUMVFs. An example is given to illustrate the usefulness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

用SURE引导自适应采样与采样颜色直方图重构

针对现有采样颜色直方图重构缺少自适应采样方法的问题,提出一种基于Stein’s unbiased risk estimator（SURE）的像素均方误差计算方法.首先对图像空间粗采样;然后在GPU上分块计算SURE来评估图像每个像素的误差度量值,并用该值引导非均匀自适应采样;最后使用非局部多尺度滤波方法重构图像.实验结果表明,不论从图像噪声定量化指标还是视觉效果来看,该方法都极大地改进了图像绘制质量.     

基于四元数的电磁矢量传感器MMSE波束形成算法

为了提高电磁矢量传感器波束形成的抗干扰能力,根据四元数具有更强的正交约束的这一特性,故四元数能更好地描述电磁矢量传感器的正交结构;因此,首先建立了四元数电磁矢量传感器模型,在此模型基础上推导出四元数MMSE(最小均方误差)波束形成算法;对于复数和四元数两种算法而言,在同等低快拍数的情况下,基于四元数的MMSE波束形成算法比复数MMSE波束形成算法的输出信干噪比(SINR)大,而最小均方误差(MMSE)小;因此四元数方法响应速度快,滤波效果更好,抗干扰能力更强。仿真实验验证了算法有效性。