A generalized autocovariance least-squares method for Kalman filter tuning

This paper discusses a method for estimating noise covariances from process data. In linear stochastic state-space representations the true noise covariances are generally unknown in practical applications. Using estimated covariances a Kalman filter can be tuned in order to increase the accuracy of the state estimates. There is a linear relationship between covariances and autocovariance. Therefore, the covariance estimation problem can be stated as a least-squares problem, which can be solved as a symmetric semidefinite least-squares problem. This problem is convex and can be solved efficiently by interior-point methods. A numerical algorithm for solving the symmetric is able to handle systems with mutually correlated process noise and measurement noise.     

Analysis of the correlation between acoustic noise and vibration generated by a multi-layer ceramic capacitor

Acoustic noise generated by a multi-layer ceramic capacitor (MLCC) makes users uncomfortable, so the problem must be analyzed to reduce the noise. There is a correlation between the acoustic noise and the vibration of MLCCs and the circuit board. Therefore, the acoustic noise problem must be investigated from a vibration perspective. In this study, the acoustic noise-generating mechanism was investigated, and the relationship between the characteristics of the noise and the dynamic characteristics of the circuit board with MLCC was analyzed. And a correlation criterion was proposed to predict the acoustic noise using the vibration response of the circuit board.     

近邻感知的标签噪声过滤算法

基于k近邻的标签噪声过滤对近邻参数k的选取较敏感.针对此问题,文中提出近邻感知的标签噪声过滤算法,可有效解决二分类数据集的类内标签噪声的问题.算法分开考虑正类样本和负类样本,使分类问题中的标签噪声检测问题转化为两个单类别数据的离群点检测问题.首先通过近邻感知策略自动确定每个样本的个性化近邻参数,避免近邻参数敏感的问题.然后根据噪声因子将样本分为核心样本与非核心样本,并把非核心样本作为标签噪声候选集.最后结合候选样本的近邻标签信息,进行噪声的识别与过滤.实验表明,文中方法的噪声过滤效果和分类预测性能均较优.     

基于极大似然准则和最大期望算法的自适应UKF 算法

针对噪声先验统计特性未知情况下的非线性系统状态估计问题,提出了基于极大似然准则和 最大期望算法的自适应无迹卡尔曼滤波(Unscented Kalman filter, UKF) 算法.利用极大似然准则构造含有噪声统计特性的对数似然函数,通 过最大期望算法将噪声估计问题转化为对数似然函数数学期望极大化问题,最终得到带次优递 推噪声统计估计器的自适应UKF算法.仿真分析表明,与传统UKF算法相比,提出的自适应UKF算法 有效克服了传统UKF算法在系统噪声统计特性未知情况下滤波精度下降的问题,并实现了系统噪 声统计特性的在线估计.     

Mixed H2/H∞ filtering

In paper we consider the problem of finding a filter or estimator that minimizes a mixed H₂/H_∞ filtering cost on the transfer matrix from a given noise input to the filtering error subject to an H_∞ constraint on the transfer matrix from a second noise input to the filtering error. This problem can be interpreted and motivated in many different ways; for instance, as a problem of optimal filtering in the presence of noise with fixed and known spectral characteristics subject to a bound on the filtering error due to a second noise source whose spectral characteristics are unknown. It is shown that one can come arbitrarily close to the optimal mixed H₂/H_∞ filtering cost using a standard Kalman-Luenberger estimator. Moreover, the problem of finding suitable Kalman-Luenberger estimator gains can be converted into a convex optimization problem involving affine symmetric matrix inequalities.     

应用于地震数据去卷的自校正白噪声估值器

本文把地震数据去卷问题处理为估计带观测噪声的ARMA模型的白噪声问题,应用时间序列分析方法提出了不同于Mendel的新的稳态最优白噪声估值器,文章基于两个ARMA新息模型的在线辨识,进一步给出了自校正白噪声估值器.     

Noncausal minimax linear state estimation for systems with uncertain second-order statistics

This note considers the problem of minimax state estimation of the states of a linear time-invariant system which is driven by and observed in the presence of noise processes with uncertain second-order statistics. When the process noise and observations are scalars, the problem is shown to be equivalent to a scalar minimax estimation problem. The existence of a minimax solution is thereby established, and the minimax filter is shown to be a linear transformation of the minimax filter for the scalar problem.     

带有色厚尾量测噪声的鲁棒高斯近似滤波器和平滑器

为了解决带有色厚尾量测噪声的非线性状态估计问题,本文提出了新的鲁棒高斯近似（Gaussian approximate,GA）滤波器和平滑器.首先,基于状态扩展方法将量测差分后带一步延迟状态和白色厚尾量测噪声的非线性状态估计问题,转化成带厚尾量测噪声的标准非线性状态估计问题.其次,针对量测差分后模型中的噪声尺度矩阵和自由度（Degrees of freedom,DOF）参数未知问题,设计了新的高斯近似滤波器和平滑器,通过建立未知参数和待估计状态的共轭先验分布,并利用变分贝叶斯方法同时估计未知的状态、尺度矩阵、自由度参数.最后,利用目标跟踪仿真验证了本文提出的带有色厚尾量测噪声的鲁棒高斯近似滤波器和平滑器的有效性以及与现有方法相比的优越性.     

基于独立分量的信号分离算法研究

噪声控制工程中,定量分离主要噪声源是一个非常复杂的问题,国内外对此进行了大量研究,并提出了许多算法,但基本都只适用于线谱,对于宽带信号显得无能为力。据此,基于独立分量分解原理,尝试通过信号分解、线谱提取的方式,将宽带噪声从原信号中准确分离出来,然后可以依据相干分析和谱分析等手段定量分析。计算机仿真研究效果良好,证明该方法具有一定工程应用价值。     

Understanding the role of noise in stochastic local search: Analysis and experiments

Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches to solving computationally hard problems. SLS algorithms typically have a number of parameters, optimized empirically, that characterize and determine their performance. In this article, we focus on the noise parameter. The theoretical foundation of SLS, including an understanding of how to the optimal noise varies with problem difficulty, is lagging compared to the strong empirical results obtained using these algorithms. A purely empirical approach to understanding and optimizing SLS noise, as problem instances vary, can be very computationally intensive. To complement existing experimental results, we formulate and analyze several Markov chain models of SLS in this article. In particular, we compute expected hitting times and show that they are rational functions for individual problem instances as well as their mixtures. Expected hitting time curves are analytical counterparts to noise response curves reported in the experimental literature. Hitting time analysis using polynomials and convex functions is also discussed. In addition, we present examples and experimental results illustrating the impact of varying noise probability on SLS run time. In experiments, where most probable explanations in Bayesian networks are computed, we use synthetic problem instances as well as problem instances from applications. We believe that our results provide an improved theoretical understanding of the role of noise in stochastic local search, thereby providing a foundation for further progress in this area.     

A separation theorem for the stochastic sampled-data LQG problem†

This paper considers the control of a continuous linear plant disturbed by white plant noise when the control is constrained to be a piecewise constant function of time: i.e. a stochastic sampled-data system. The cost function is the integral of quadratic error terms in the state and control, thus penalizing errors at every instant of time while the plant noise disturbs the system continuously. The problem is solved by reducing the constrained continuous problem to an unconstrained discrete one. It is shown that the separation principle for estimation and control still holds for this problem when the plant disturbance and measurement noise are Gaussian.     

Linear quadratic regulation problem for discrete-time systems with multi-channel multiplicative noise

This paper investigates the linear quadratic regulation (LQR) problem for discrete-time systems with multiplicative noise. Multiplicative noise is usually assumed to be a scalar in existing literature works. Motivated by recent applications of networked control systems and MIMO communication technology, we consider multi-channel multiplicative noise represented by a diagonal matrix. We first show that the finite horizon LQR problem can be solved using a generalized Riccati equation. We then prove the convergence of the generalized Riccati equation under the conditions of stabilization and exact observability, and obtain the solution to the infinite horizon LQR problem. Finally, we provide a numerical example to demonstrate the proposed approach.     

有色量测噪声下的HCKF及其应用

针对高阶容积卡尔曼滤波（HCKF）算法在有色量测噪声条件下滤波精度下降的问题,提出了有色量测噪声下的HCKF算法。通过一阶马尔科夫模型将有色量测噪声进行白化,将带有色量测噪声的非线性离散随机系统转化为白噪声下的非线性时滞系统,并给出高斯域内针对非线性时滞系统的贝叶斯滤波框架。利用高阶容积准则对该滤波框架进行近似计算,进而得到有色量测噪声下的HCKF算法。将所提算法应用到机动目标跟踪系统中,仿真实验结果表明,量测噪声为白噪声时,所提算法与标准HCKF算法具有相同的估计性能;在量测噪声为有色噪声时,所提算法相比于标准HCKF具有更优的估计精度和鲁棒性。     

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.

     

State estimation for multi‐channel stochastic singular systems with multiplicative noise

The state estimation problem for multi‐channel singular systems with multiplicative noise is considered based on singular value decomposition. First, two equivalent reduced order subsystems are obtained via the decomposition. Then, in order to solve the estimation problem, the subsystems are rewritten into a new form. It is noted that the measurement noise here becomes colored noise, which contains the dynamic noise, measurement noise, and multiplicative noise of the original system. In this situation, existing filtering methods cannot be directly applied, so a modified filtering method is given. The recursive algorithm for the state estimation is obtained by the filtering method. In addition, the estimation of dynamic noise is derived via the algorithm. A simulation example is given to show the effectiveness of the proposed algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Robust nonlinear disturbance suppression of a magnetic levitation system

In this paper the problem of noise suppression for a magnetic levitation system is addressed. The problem is cast as a nonlinear regulation problem and an internal model-based regulator able to offset the noise in spite of the presence of unknown parameters affecting the model of the system is designed. The controller is designed using nested saturation functions and is able to provide a global region of attraction. Simulation results confirm the effectiveness of the design.     

Optimum decoding based smoothing algorithm for dynamic systems with interference

This paper presents a new smoothing algorithm for discrete models with arbitrary random interference. For the disturbance noise, observation noise and interference, only independency is required. Moreover, the motion and observation models are not restricted to be linear functions of the disturbance noise and interference. This algorithm estimates the states by reducing the smoothing problem to a multiple composite hypothesis testing problem, and then using the Viterbi decoding algorithm. Simulation results have shown that the new algorithm performs very well.     

基于滤波与曲线拟合的电子稳像算法

针对目前电子稳像算法无法同时去除高频噪声与低频噪声的问题,提出了可同时去除高频噪声和低频噪声的滤波与曲线拟合相结合的方法。该方法首先用位平面匹配算法快速估计出帧间的偏移量;其次对帧间偏移量进行累加,计算出当前帧相对于参考帧的全局运动量,并对全局运动量进行卡尔曼滤波,以去除高频噪声;最后,对卡尔曼滤波的结果进行曲线拟合以去除低频噪声;最终,得到稳定的主运动轨迹。实验证明,该方法可以有效地去除高频和低频噪声,视频稳定效果良好。     

线性均方一致性问题的偏差估计

多智能体协同在传感网、社交网、分布式控制等诸多领域有着广泛的实际应用背景,一致性问题作为多智能体协同的基础,受到越来越多研究者的关注.在实际环境中,由于设备、通信干扰等诸多原因,信息在传递过程中通常会携有噪声,本文对噪声条件下一致性问题的系统偏差进行了研究,将求解一致性协议噪声偏差问题转化成矩阵范数的积分问题,根据矩阵迹与特征值的关系,利用范数不等式及积分中值定理,给出仅与增益函数和网络结构相关的一致性协议噪声偏差上界,为一致性系统在实际应用中的噪声估计奠定了理论基础.     

Robust regression for face recognition

In this paper we address the problem of robust face recognition by formulating the pattern recognition task as a problem of robust estimation. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace (Barsi and Jacobs, 2003 [1]), we develop a linear model representing a probe image as a linear combination of class specific galleries. In the presence of noise, the well-conditioned inverse problem is solved using the robust Huber estimation and the decision is ruled in favor of the class with the minimum reconstruction error. The proposed Robust Linear Regression Classification (RLRC) algorithm is extensively evaluated for two important cases of robustness i.e. illumination variations and random pixel corruption. Illumination invariant face recognition is demonstrated on three standard databases under exemplary evaluation protocols reported in the literature. Comprehensive comparative analysis with the state-of-art illumination tolerant approaches indicates a comparable performance index for the proposed RLRC algorithm. The efficiency of the proposed approach in the presence of severe random noise is validated under several exemplary noise models such as dead-pixel problem, salt and pepper noise, speckle noise and Additive White Gaussian Noise (AWGN). The RLRC algorithm is found to be favorable compared with the benchmark generative approaches.