一种MEMS加速度计误差分析与校准方法

MEMS传感器测量中一般存在确定性偏差和随机噪声两类误差,为提高其测量精度和稳定性,以MEMS加速度计为例,在建立其测量误差模型的基础上,提出一个综合的校准方案,同时对这两类误差进行补偿。其中使用Allan方差对随机噪声项进行量化分析,并综合采用了最小二乘法计算确定性校准系数,扩展卡尔曼滤波降低随机噪声等方法。通过实验验证,表明该方案可以有效校准加速度计测量的确定性偏差并降低随机噪声干扰,最终经过误差补偿后的测量数据在精度和稳定性方面都有明显提升。     

Evolution of system noises in electrodeposition process control

The evolution of source noises in space–time is analysed in this paper. It characterises the global effect of uncertainties in electrodeposition process control, where the source noises have an effect on the concentration field of relevant species in the diffusion layer and the field is controlled by the Neumann boundary using relatively simple boundary controls. The control errors evolve in the diffusion layer and are dependent upon the source noises and applied controls as a random field process. The covariance structure of the field is found analytically and confirmed numerically. The local source noises are incited by the uncertainties from a realistic control system; they are devised by the process physics and a control system structure. This paper demonstrates that even in a relatively simple system, the local uncertainties have a strong tendency to expand in space–time. Some source noises have a dispersed effect on the overall system uncertainty (control error), others are more local and do not expand in the same way. The noise of the mass flux, which is injected through the Neumann boundary, dies out quickly in the diffusion layer.     

焊接机器人图像传感器噪声分析

焊接机器人获取焊缝的计算机视觉信息过程中,由于工件表面的反光系数,光学镜头性能的非线性,光电转换时叠加了随机噪声;信号电荷的存储、传输和输出存在暗电流等噪声;视频信号传输时存在噪声;视频信号模数转换存在量化噪声,导致获取的二维数字图像信号有一定的误差。为了减小此误差对后续的图像处理的影响,对图像传感器的图像噪声进行了分析,并得到了一个经验的图像噪声模型,实验结果表明:该模型有效且实用。     

加速度计信号处理的建模与仿真

研究车载公路路面平整度动态测量系统的优化设计问题,加速度计输出信号的二次积分用于修正路面高程数值,由于目前处理一维加速度计信号的方法中均不能滤除混入的标度因数误差。为解决上述问题,提出根据卡尔曼滤波原理建立标定车载道路路面平整度检测单元中一维加速度计混入的零偏差和标度因数误差的卡尔曼滤波模型,并使用MATLAB软件上进行仿真,仿真结果表明建立的卡尔曼滤波模型可以有效估计和滤除加速度计输出信号中混入的固有零偏差、标度因数误差和随机白噪声,为优化设计提供了依据。     

Improved IMM Filter for Tracking a Highly Maneuvering Target with Mixed System Noises

We propose a method of improving tracking filter performance of a highly maneuvering target with mixed system noises in this paper. A case study of an off-road high speed moving target is considered. The system noises consist of white Gaussian noises generated from target motion models and additional colored noises arising from the effect of rough and uneven terrain profile. we design the colored noise first order discrete Markov dynamic system representing terrain conditions. Tracking is done by using an IMM filter with discrete white noise acceleration and horizontal coordinated turn models. The designed colored noise dynamic model is augmented with each of the motion models. We use Kalman filter for linear DWNA model while extended and unscented Kalman filters are used for nonlinear HCT model. A test scenario is setup and simulations are carried out. For filter performance comparison purposes, two more cases are considered i.e., systems with white noncorrelated system noises and the system correlated noise cases. Results show that the proposed method outperforms the traditional error treatment methods in terms of robustness, small mean square error, and acceptable computation load and data processing time.     

阈值去噪与RBF神经网络在MEMS陀螺仪误差补偿中的应用

针对现有MEMS陀螺仪中随机误差较大,导致器件输出信噪比低进而影响其应用范围的现状,提出一种基于小波阈值去噪与梯度径向基( RBF)神经网络结合的MEMS陀螺漂移非平稳时间序列建模预测方法。首先采用Allan方差法分析了MEMS陀螺仪的主要随机误差,随后利用小波阈值去噪分离出MEMS陀螺误差模型中的白噪声及漂移误差,最后采用RBF神经网络对漂移数据进行建模。通过实验对文中所述的误差补偿方法进行验证,表明了方法的有效性,对于基于MEMS陀螺仪的惯导系统精度提高具有重要意义。     

基于无偏置项LSSVM的稳健在线过程建模方法

针对直接利用最小二乘支持向量机(LSSVM)对动态过程在线建模时预测精度易受过程输出测量值上的粗大误差和噪声影响的问题,在分析样本序列结构特征和噪声作用特征基础上,提出一种基于无偏置项LSSVM的稳健在线过程建模方法。该方法在每一预测周期中根据预测误差与设定阈值之间的关系来识别和恢复异常测量值、识别和修正含噪声测量值,从而降低样本中的噪声,使得出的LSSVM较好地跟踪过程的动态特性。这种在线过程建模方法具有稳健性,能减少输出值上粗大误差和高斯白噪声对LSSVM预测精度的影响,提高预测精度。数字仿真显示该方法的有效性和优越性。     

Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor

A unified multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. The criterion considers the correlation among local estimation errors, only requires the computation of scalar weights, and avoids the computation of matrix weights so that the computational burden can obviously be reduced. Based on this fusion criterion and Kalman predictor, an optimal information fusion filter for the input white noise, which can be applied to seismic data processing in oil exploration, is given for discrete time-varying linear stochastic control systems measured by multiple sensors with correlated noises. It has a two-layer fusion structure. The first fusion layer has a netted parallel structure to determine the first-step prediction error cross-covariance for the state and the filtering error cross-covariance for the input white noise between any two sensors at each time step. The second fusion layer is the fusion center to determine the optimal scalar weights and obtain the optimal fusion filter for the input white noise. Two simulation examples for Bernoulli-Gaussian white noise filter show the effectiveness.     

A new information fusion white noise deconvolution estimator

The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, a new information fusion white noise deconvolution estimator is presented for the general multisensor systems with different local dynamic models and correlated noises. It can handle the input white noise fused filtering, prediction and smoothing problems, and it is applicable to systems with colored measurement noises. It is locally optimal, and is globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. In order to compute the optimal weights, the formula computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with Bernoulli-Gaussian input white noise shows the effectiveness and performances.     

Non-stationary stochastic embedding for transfer function estimation

This paper presents a consistent framework for the quantification of noise and undermodelling errors in transfer function model estimation. We use the, so-called, “stochastic embedding” approach, in which both noise and undermodelling errors are treated as stochastic processes. In contrast to previous applications of stochastic embedding, in this paper we represent the undermodelling as a multiplicative error characterised by random walk processes in the frequency domain. The benefit of the present formulation is that it significantly simplifies the estimation of the parameters of the embedded process yielding a closed-form expression for the model error quantification. Simulation and experimental examples illustrate how the random walks effectively capture typical cases of undermodelling found in practice, including underdamped modes. The examples also show how to use the method as a tool in the determination of model order and pole location in fixed denominator model structures.     

A discontinuous Galerkin method for stochastic Cahn–Hilliard equations

In this paper, a discontinuous Galerkin method for the stochastic Cahn-Hilliard equation with additive random noise, which preserves the conservation of mass, is investigated. Numerical analysis and error estimates are carried out for the linearized stochastic Cahn-Hilliard equation. The effects of the noises on the accuracy of our scheme are also presented. Numerical examples simulated by Monte Carlo method for both linear and nonlinear stochastic Cahn-Hilliard equations are presented to illustrate the convergence rate and validate our conclusion.     

MEMS传感器随机误差分析及处理

MEMS传感器的精度限制了其应用范围,为减小MEMS传感器随机误差的影响,提高其精度,对其随机误差进行分析和处理具有重要意义。本文首先采用Allan方差法分析了MEMS陀螺仪和加速度计的主要随机误差。然后基于Allan方差分析结果,发现从时频角度,采用小波变换分析传感器的随机误差,可以对零偏,有色噪声,白噪声进行了有效分离。最后通过对小波降噪方法的探讨,发现小波分析具有分离、减小随机误差的优势。     

Identification of EIV models by compensated PEM

This paper deals with identification of discrete-time errors-in-variables models where the input and output data are both perturbed by different additive noises. The goal is to study the effects of input noise on the model which is estimated based on the prediction error method. The obtained model is then improved by modifying the results and implementing the instrumental variable method. It is proved that the identification of the errors-in-variables models based on the proposed approach could result in an unbiased estimation in the presence of independent colour noises on the input and output data with adequate accuracy and mediocre complexity.     

统一的快速次优固定区间白噪声Wiener平滑算法

对带相关噪声的线性离散随机控制系统,应用Kalman滤波方法,基于CARMA新息模 型导出了统一的最优固定区间白噪声递推Wiener平滑器,它带有系数阵指数衰减到零的高阶多 项式矩阵.进一步用截断方法提出了相应的快速次优固定区间自噪声Wiener平滑算法,它显著 地减小了计算负担.给出了平滑误差公式和选择截断指数的公式.一个Bernoulli-Gaussian白噪声 的仿真例子说明了所提出的结果的有效性.     

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises, and with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers–Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli–Gaussian input white noise shows its effectiveness.     

A stochastic model for electrodeposition process with applications in filtering and boundary control

Electrodeposition is a complex partially observed mass-transfer process driven by several surface reactions without exact model. In this article, the process uncertainties are described by a finite number of Wiener processes in a stochastic model applied in the filtering and control problems. These problems are solved as a boundary observation-control problem based on a finite diffusion model with uncertainties in the domain interior and on the boundaries. A mixed boundary problem is considered on an interval with the Dirichlet data on one end (bulk solution) and Neumann data on the other end (cathode surface). The concentration of oxidising species in the domain interior is unattainable for observations but the flux on the boundary (electric current) can be measured with a limited accuracy (sensor error). The total flux for the main and side reactions is controlled by the current density on the cathode surface. The disturbing effect of the side reactions is modelled as a noise. The concentration of species is stabilised at the desired level near to the cathode surface with a relatively simple feedback control. The concentration on the boundary and in the domain is estimated as a conditionally Gaussian process in the course of filtering. The estimated conditional mean of concentration is solved from a stochastic partial differential equation in dependence on the covariance kernel. A relatively good quality of estimation and control is demonstrated in the process of simulation in the realistic conditions for a copper deposition process.     

Performance loss and design method of Kalman filters for discrete-time linear systems with uncertainties†

This paper is divided into two parts. The first part is concerned with the performance loss of the discrete-time Kalman filter designed on the basis of the model with errors in both dynamical and observation systems. The difference equation which describes the evolution of the covariance matrix of actual estimation error is derived. Some numerical results are shown as the illustration of the technique.

The second half is devoted to the development of the method of designing the unbiased minimum variance linear filter for the random system whose elements of both the transition and observation matrices are Gaussian white noises. For this purpose the result of the first part is utilized.     

Critical Issues on Kalman Filter with Colored and Correlated System Noises

The Kalman filtering (KF) is optimal under the assumption that both process and observation noises are independent white Gaussian noise. However, this assumption is not always satisfied in real‐world navigation campaigns. In this paper, two types of KF methods are investigated, i.e. augmented KF (AKF) and the second moment information based KF (SMIKF) with colored system noises, including process and observation noises. As a popular noise‐whitening method, the principle of AKF is briefly reviewed for dealing with the colored system noises. The SMIKF method is developed for the colored and correlated system noises, which directly compensates for the covariance through stochastic model in the sense of minimum mean square error. To accurately implement the SMIKF, a refined SMIKF is further derived regarding the continuous‐time dynamic model rather than the discrete one. The computational burdens of the proposed SMIKF along with representative methods are analyzed and compared. The simulation results demonstrate the performances of proposed methods.     

基于K近邻算法的噪声种类识别和强度估计

对于目前噪声种类识别和强度估计方法都是针对单噪声,无法估计混合噪声中源噪声的强度的问题,提出了一种有距离阈值的K近邻（KNN）算法,实现对单噪声和混合噪声的种类识别,并结合混合噪声识别结果和噪声基重构估计混合噪声中源噪声的强度。首先,选用频域数据分布作为特征向量;然后,采用噪声种类识别算法进行种类识别,并且在噪声基重构过程中以重构噪声与真实噪声的频域余弦距离作为强度估计算法的最优化评价标准;最后,实现对源噪声强度的估计。在两个测试数据库上的实验结果表明,所提算法的噪声种类识别的平均精度高达98.135%,混合噪声强度估计的误差率为20.96%。实验结果验证了噪声种类识别算法的准确性和泛化性,以及混合噪声强度估计算法的可行性,并且该方法为混合噪声强度估计提供了新思路。采用该方法获取的混合噪声种类和强度信息有助于去噪方法和去噪参数的确定,进而提高去噪效率。