单传感器多尺度状态融合估计算法

该文在离散小波变换理论和动态多尺度系统理论的基础上,建立了一种基于单传感器的多尺度状态融合估计新算法。该方法利用离散小波变换,对Kalman滤波模型的状态方程和观测方程分别进行多尺度处理,构建多尺度Kalman滤波模型,充分利用状态估计和观测数据在不同尺度上的特征进行融合估计,获得了优于单尺度Kalman滤波及已有多尺度状态融合估计方法的处理效果。并利用Monte Carlo仿真验证该算法的有效性。      

多分辨率传感器多尺度融合估计算法

该文在基于信号统计特性的离散小波变换理论(DWT)和基于状态转移模型的动态多尺度系统理论(DMS)的基础上,提出了一种多分辨率传感器的多尺度状态融合估计新算法.该方法利用离散小波变换,首先对不同分辨率传感器Kalman滤波模型的状态方程和观测方程分别进行多尺度处理,构建统一的多尺度Kalman滤波模型,然后将不同分辨率...     

杂波抑制的分形处理:小波-多尺度自适应Kalman滤波

从杂波分形模型的角度,以基于小波变换和多尺度自适应Kalman滤波的方法,解决雷达信号处理中重要的杂波抑制问题。首先对接收信号作小波分解,利用小波系数建立状态方程和观测方程,用Kalman滤波对每一尺度估计分形杂波,然后从接收信号中减去估计得到的分形杂波,从而实现杂波抑制。仿真结果表明,基于小波变换和多尺度Kalman滤波的处理方法,能对分形杂波进行有效的估计分析,进而实现有效的抑制。     

一类带有等式约束的动态系统的滤波方法

在动态系统的状态受到约束的时候,传统的卡尔曼滤波器无法直接的应用.本文提出了一种多尺度约束滤波算法来处理一类由等式方程描述的约束问题.通过对系统状态的分块和小波变换,建立一个包含约束条件的新的动态系统,然后对小波系数进行滤波,最后通过重构得到原系统的估计值.新算法的估计过程是实时的,并且具有多尺度分析的能力.利用约束条...     

单传感器单模型动态系统多尺度分解与估计新算法

本文将基于模型的动态系统分析方法与具有统计特性的多尺度信号变换方法相结合,基于某一尺度上给定的单传感器单模型动态系统,建立起一个新的多尺度动态模型。基于建立的多尺度模型和标准Kalman滤波,提出一个能同时对随机信号进行多尺度分解与多尺度估计的新算法。获得比仅在原始尺度上进行Kalman滤波好的处理效果,应用Monte Carlo仿真验证了算法的有效性。     

单传感器单模型动脉系统多尺度分解与估计新算法

本文将基于模型的动态系统分析方法与具有统计特性的多尺度信号变换方法相结合，基于某一尺度上给定的单传感器单模型动态系统，建立起一个新的多尺度动脉模型，基于建立的多尺度模型和标准Kalman滤波，提出一个能同时对随机信号进行多尺度分解与多尺度估计的新算法，获得比仅在原始尺度上进行Kalman滤波好的处理效果，应用Monte Carlo仿真验证了算法的有效性。     

修正Kalman滤波多带UWB信道估计改进方法

针对多带超宽带(UWB)系统中修正Kalman滤波算法复杂度高的缺陷,提出一种低复杂度的修正Kalman滤波改进方法。该方法中UWB信道采用自回归模型(AR)建模,利用导频跟踪时变信道衰减因子,通过Kalman滤波和频域分段最小均方误差(MMSE)算法同时跟踪信道的时域相关性和频域相关性,提高了系统性能,降低了计算复杂度。仿真结果表明,和修正的Kalman滤波方法相比,在估计精度损失很小的情况下,所提方法极大降低了计算复杂度,提高了系统整体的估计性能。     

多传感器单模型动态系统多尺度数据融合

利用多尺度分析的思想,将基于模型的动态系统分析方法与基于统计特性的多尺度信号变换方法相结合,建立起目标状态基于多源观测信息的多尺度数据融合估计新算法,在每个尺度上获得目标状态基于全体细尺度上传感器测量信息的融合估计值,此算法可有效地应用于拥有不同采样速率的多传感器动态系统的数据融合估计.     

具有输入信号的自适应跟踪滤波器

针对导弹在被动段自由飞行的状态,提出了一种有效的基于输出残差的自适应跟踪滤波器.首先给出了飞行状态方程,其次利用输出残差给出了Kalman滤波增益马尔可夫参量估计,并且得到了Kalman滤波增益估计,解决了自适应跟踪滤波器问题.仿真结果说明了此方法的有效性.     

基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计

本文将强跟踪滤波理论与多传感器数据融合技术相结合,提出基于强跟踪滤波器的多传感器状态与参数联合估计新算法;对拥有相同采样率的分布式多传感器单模型非线性动态系统,应用强跟踪滤波器,得到目标状态基于全局信息融合估计结果,并利用计算机仿真结果对算法的有效性进行了验证;这些工作初步解决了Kalman滤波中由于模型的不确定性而造成估计误差值偏大情况下的状态融合估计问题,从而丰富和发展了多源信息融合理论.     

基于多尺度特征提取的Kalman滤波跟踪

针对波动性较大目标跟踪,传统Kalman滤波算法鲁棒性和实时性不足,提出一种基于多尺度特征提取的Kalman跟踪算法.前帧目标区域特征点匹配出后续帧目标区域特征点,并以后者特征点为中心,建立搜索区域,避免了遍历整幅后续帧图像,快速地为Kalman滤波方程状态后验值提供了稳定的观测信号和观测残差.实验证明,这种作为约束条...     

Spatially adaptive wavelet-based multiscale image restoration

In this paper, we present a new spatially adaptive approach to the restoration of noisy blurred images, which is particularly effective at producing sharp deconvolution while suppressing the noise in the flat regions of an image. This is accomplished through a multiscale Kalman smoothing filter applied to a prefiltered observed image in the discrete, separable, 2-D wavelet domain. The prefiltering step involves constrained least-squares filtering based on optimal choices for the regularization parameter. This leads to a reduction in the support of the required state vectors of the multiscale restoration filter in the wavelet domain and improvement in the computational efficiency of the multiscale filter. The proposed method has the benefit that the majority of the regularization, or noise suppression, of the restoration is accomplished by the efficient multiscale filtering of wavelet detail coefficients ordered on quadtrees. Not only does this lead to potential parallel implementation schemes, but it permits adaptivity to the local edge information in the image. In particular, this method changes filter parameters depending on scale, local signal-to-noise ratio (SNR), and orientation. Because the wavelet detail coefficients are a manifestation of the multiscale edge information in an image, this algorithm may be viewed as an "edge-adaptive" multiscale restoration approach.     

1/f分形噪声的一种多尺度Kalman滤波方法

针对淹没在1／f分形噪声中的有用信号恢复问题，提出了一种基于小波变换与Kalman滤波的多尺度滤波算法。首先将带有1／f分形噪声的信号分解成多尺度的子带信号，通过小波变换对1／f分形噪声的白化作用，消除了1／f分形噪声的自相似性和长程相关性。然后在小波域内，利用Kalman滤波实现了噪声和有用信号的分离，估计出了各子带中的有用信号。最后进行小波重构，较好地恢复出淹没在1／f分形噪声中的有用信号。仿真实验表明，使用多尺度Kalman滤波器能有效地抑制分形噪声，显著地提高了信噪比。     

UKF在GPS/INS伪距、伪距率组合导航中的应用

扩展卡尔曼滤波（EKF）是GPS／INS组合导航系统中常用的数据融合方式。但是EKF的线性化会带来截断误差,从而影响系统定位精度。不敏卡尔曼滤波（UKF）是一种新的非线性滤波的方法,它能减少线性化截断误差对系统定位精度的影响。文中在线性状态方程的条件下,主要研究了伪距、伪距率的非线性对系统定位性能的影响。UKF采用非线性观测方程,EKF采用线性观测方程。仿真结果表明UKF能明显改善位置项的定位精度。     

基于卡尔曼滤波的剩余寿命预测模型

振动烈度是剩余寿命的一个评价指标。为提高剩余寿命预测精度,解决时序模型预测延时问题,文中提出了一种时间序列分析理论,对振动烈度数据进行平稳建模,得到符合其变化规律的模型方程;通过得到的模型方程推导出卡尔曼滤波算法的状态方程和观测方程;然后依靠卡尔曼预测递推方程进行预测,再对振动烈度进行预测,从而预测剩余寿命。实例分析表明,采用混合算法可以提高预测精度,且较好地解决了预测延时问题。     

Intelligent Dynamic Radio Tracking in Indoor Wireless Local Area Networks

Indoor positioning is an enabling technology for delivery of location-based services in mobile computing environments. This paper proposes a positioning solution using received signal strength in indoor Wireless Local Area Networks. In this application, an explicit measurement equation and the corresponding noise statistics are unknown because of the complexity of the indoor propagation channel. To address these challenges, we introduce a new state-space Bayesian filter: the Nonparametric Information (NI) filter. This filter effectively tracks motion in situations where the Kalman filter and its variants are inapplicable, while maintaining a computational complexity comparable to that of the Kalman filter. To deal with the noisy nature of the indoor propagation environment, the NI filter is used in the design of an intelligent dynamic WLAN tracking system. The system anticipates future position values and adapts its sensing and estimation parameters accordingly. Our experimental results conducted on measurements from a real office environment indicate that the combination of the intelligent design and the NI filter results in significant improvements over the Kalman and particle filters.     

Spatio-temporal adaptive 3-D Kalman filter for video

This paper presents three-dimensional (spatio-temporal) Kalman filters for video as the extension of the two-dimensional (2-D) reduced update Kalman filter (RUKF) approach for images. We start out with three-dimensional (3-D) RUKF, a shift-invariant recursive estimator with efficiency advantages over the 3-D Wiener filter. Then, we turn to the motion-compensated extension MC-RUKF, which gives improved performance when coupled with a motion estimator. Since motion compensation sometimes fails, causing severe fluctuations in temporal correlation, we then present multimodel MC-RUKF, to adapt to variation in temporal and spatial correlation, by detecting the local image model out of a class, and using it in MC-RUKF. Finally, we introduce a novel multiscale model detection algorithm for use in high noise environments.     

应用卡尔曼滤波技术的激光熔覆宽度检测

对金属零件激光成形过程闭环控制系统中,熔覆宽度的检测技术进行了研究,提出了一种基于卡尔曼滤波技术的熔覆宽度检测方法。利用视觉传感系统获取激光加工过程中的熔池图像,经过图像处理与图像标定求熔覆宽度作为参量建立系统状态方程和测量方程,应用卡尔曼滤波算法对图像上的熔宽和熔宽变化进行状态估计,得到最小均方差条件下的熔覆宽度最佳预测值,从而减小过程噪声和测量噪声引起的熔覆宽度测量偏差,测量平均误差由0.028 mm降为0.009 3 mm,实现加工过程熔覆宽度的精确检测。实验结果证明:将卡尔曼滤波技术应用到熔覆宽度检测过程中可以大大提高熔宽检测精度。     

Self-tuning measurement fusion Kalman filter with correlated measurement noises

For the multisensor system with correlated measurement noises and unknown noise sta-tistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.     

Rotation rate estimation in parametrically excited micro gyroscopes

This paper reports the estimation of angular velocity in micro gyroscopes with parametric excitation. The identification procedure is done into two consecutive steps: In the first step, the physical parameters of the gyroscope (stiffness, damping, and actuator parameters) are estimated via continuous time Extended and Unscented Kalman filter. In the second step, a separate Kalman filter is dedicated to estimate the time varying rotation rate, using the output of the first step. Using numerical simulations, it is found that by introducing an artificial noise in the observer equations and tuning its variance, arbitrary temporal variation of angular velocity can be well tracked by the proposed observer.