飞行器仿真测试中合作目标投影光斑的跟踪识别

针对飞行器仿真测试中合作目标投影光斑的跟踪识别问题,提出了一种投影光斑跟踪识别新方法,该方法主要由预测、识别及修正3个阶段组成。预测阶段主要结合投影光斑运动特点对传统卡尔曼滤波进行改进,提高投影光斑的位置预测精度。识别阶段则根据投影光斑位置的预测值,分两种情况对光斑进行处理:如果下一时刻投影光斑在视场内,则根据相应的判别准则和匹配策略在下一时刻图像中快速搜索投影光斑的最优匹配光斑;如果下一时刻投影光斑在视场外,则根据测量系统相关信息,对视场外投影光斑在图像平面上的位置进行求解,并将求解结果加入相应的运动轨迹,实现对视场外投影光斑的跟踪识别。在完成投影光斑的跟踪识别后,根据跟踪识别结果对投影光斑相关参数进行修正。仿真实验和实际实验结果表明,本文方法能够有效跟踪识别飞行器仿真测试中合作目标的投影光斑,其最大跟踪识别误差不超过2.5pixel,即使跟踪识别过程中存在投影光斑进出视场的情况也不受影响。     

自适应搜索的快速分块跟踪

针对传统的分块跟踪算法计算量大,难以实时地对运动目标进行跟踪这一问题,提出了一种改进的分块跟踪算法.首先,为降低背景噪声对跟踪性能产生的不利影响,提出的算法对目标所在矩形窗口进行了更细致的划分;然后,根据目标运动信息确定搜索范围和搜索中心,采用分层次的自适应搜索算法,在每一层采用不同的搜索策略逐步逼近与目标模板最相似的...     

利用自适应卡尔曼滤波实现光电跟踪中的复合控制

为了在光电跟踪控制系统中实现复合控制以提高跟踪精度,构建了基于模型自适应卡尔曼滤波算法的复合控制结构。首先,利用跟踪脱靶量数据和仪器位置数据合成目标角位置数据;然后,利用模型自适应卡尔曼滤波算法对目标角位置数据进行滤波估计以获得目标角速度信息;最后,将目标角速度信息前馈到速度回路,从而构成复合控制系统。实验结果表明:采用复合控制结构后,目标跟踪精度提高了50%。基于模型自适应卡尔曼滤波算法的复合控制技术能够在保持原反馈控制系统稳定性的条件下提高跟踪精度。     

改进核相关滤波器的海上船只目标跟踪

提高海上智能监测水平,为了实现对海上船只目标的跟踪,文中针对典型海况环境下的海上船只目标跟踪问题,提出了一种改进核相关滤波器(Kernelized Correlation Filters,KCF)的船只目标跟踪算法。首先,针对KCF算法的特征,提出船只目标跟踪临界概率的概念,用来判断目标跟踪是否异常;接着,加入卡尔曼滤波模块,用来预测跟踪目标下一时刻的位置;然后,对跟踪异常的目标设计目标跟踪异常处理模块进行处理;最后,针对4组典型的海上目标跟踪场景,通过实验验证了算法的性能。实验结果表明:文中算法在海上船只大幅度晃动、跟踪目标被遮挡、目标出界、目标尺寸变换等复杂情况下,跟踪准确率和速率比原KCF算法分别提高17.23%和7.86%。满足海上目标跟踪精度、实时性、适用性等方面的要求。     

基于斜率约束和回溯搜索的水下多目标跟踪方法

针对复杂水域环境下的多目标跟踪问题,本文提出了一种基于斜率约束和回溯搜索的多目标跟踪方法。首先,基于方位测量数据和水下目标运动学分析,利用门限阈值的方法检测目标。然后,基于传统多假设跟踪算法框架设计一种新的斜率约束和共用量测的假设生成规则。在航迹中断时,通过回溯搜索的方法确定中断起始航迹点,利用容积卡尔曼滤波对中断航迹预测和补偿,同时对假设生成结果减枝,以达到降低算法空间复杂度的目的。试验结果表明:该方案能够实现多目标自动关联跟踪、中断航迹自动预测、自动航迹终止等任务,目标跟踪平均均方根误差0.594 4°,算法平均运行时间0.826 5 s。     

基于视觉的移动机器人目标跟踪方法

为实现对行人目标进行快速稳定地跟踪并简化机器人系统,提出一种快速判别尺度空间相关滤波目标跟踪算法(fDSST)与卡尔曼滤波结合的跟踪方法,解决了跟踪过程中因遮挡造成的目标坐标信息丢失问题。根据相关滤波响应图的震荡剧烈程度设置遮挡判断标准,利用遮挡判断标准实现fDSST跟踪算法与卡尔曼滤波算法的切换,持续输出目标的位置坐标信息,提升了算法的鲁棒性。移动机器人根据视觉跟踪算法提供的图像坐标,利用基于图像的伺服控制策略完成对目标的跟随任务,简化了移动机器人系统结构。最后将该方法在OTB2013测试集上和移动机器人中进行实验,实验结果表明,该方法对于目标遮挡及尺度变化具有较强的鲁棒性和准确性,同时满足实时性要求。     

传感器阵列预测空域多信号分类目标定位跟踪

目标定位跟踪技术广泛应用于军事民用领域,是当前研究的热点与难点.提出了一种空域多信号分类-自回归粒子滤波(multiple signal classification autoregressive particle filter,MUSIC-ARPF)方法,定位跟踪地面目标.该方法使用多信号分类(multiple signal classification,MUSIC)算法估计目标波达方向(direction of arrival,DOA)并计算目标信号源位置,利用自回归(autoregressive model,AR)模型和粒子滤波(particle filter,PF)算法预测信号源下一时刻位置,进而自适应选择通带与阻带扇面进行空域滤波,同时调整MUSIC算法中谱峰搜索区域,提高DOA估计的分辨率,减少目标定位的扫描域.实验结果表明,空域MUSIC-ARPF方法能够减少目标定位时间,提高目标跟踪精度.     

基于模板匹配和Kalman滤波器的目标跟踪

传统的模板匹配算法在目标做简单直线运动时有很好的跟踪效果。为解决实际应用中存在的目标变形、噪声干扰等问题,采用了动态更新模板的模板匹配,即当本帧图像中目标与模板的匹配度高于阈值时,则将本帧图像中识别的目标作为下一帧图像中目标跟踪的模板;当匹配度低于阈值时,仍将本帧的模板作为下一帧的模板,避免由于噪声的干扰导致跟踪的漂移。利用Kalman滤波原理对目标的运动状态进行了预测,缩小搜索范围,减小计算量,提高了实时性。实验表明,该方法能够快速准确地跟踪运动的目标。     

转换坐标卡尔曼滤波算法在雷达数据融合中的应用

本文介绍一种二维平面上使用的转换坐标卡尔曼滤波算法在目标跟踪测量数据融合处理中的应用,并通过仿真证明该算法在目标跟踪中优于扩展卡尔曼滤波算法。     

基于指标融合的跟踪算法性能评估度量标准

为了全面评估目标跟踪算法的性能,该文提出一种融合性能指标的新度量标准.在分析最优子模式分配度量标准基础上,新度量标准定义目标扩展状态包含跟踪算法的计算量信息,并在扩展状态的基本距离中引入了计算量距离概念,将计算量指标融入新度量标准的最优子模式分配距离中.新度量标准有效融合目标跟踪算法的跟踪精度、集合势误差和计算量指标,反映了跟踪算法的全面性能.单目标和多目标环境下的仿真实验结果验证了该标准的正确性和有效性.     

光电经纬仪子母弹多目标提取方法

光电经纬仪子母弹测量系统提供子母弹弹道等参数,为弹头最终完成作战使命、达到理想毁伤效果提供第一手科学数据。本文对子母弹多目标提取算法进行了研究。首先对数字图像采用改进的最大类间方差法（OSTU）进行二值处理,分别提取目标的形心位置、目标的面积大小,同时对目标区域进行标记;设定面积阈值,根据目标的面积大小,初步判断目标粘连与遮挡;然后再根据粘连目标区域灰度直方图波峰、波谷的变化情况分离目标;目标求灰度平均值;采用Hough变换拟合算法获得目标中轴线,然后计算目标的倾角。在匹配过程中,采用卡尔曼滤波,预测目标匹配搜索区域;在获得目标形心位置、面积大小、灰度平均值、目标倾角后,利用改进的特征函数来完成序列图像子母弹多目标的数据关联,实现了子母弹多子弹目标提取。实验结果表明,目标提取的正确率可达90%。本文采用的数学模型正确、算法合理,有效的获得了序列图像子母弹多目标参数。     

实时电子稳像系统中Kalman滤波器的FPGA实现

针对实时电子稳像系统中Kalman滤波器的硬件实现问题,提出了一种Kalman滤波的FPGA实现方法。通过对电子稳像算法中Kalman滤波模型的分析,对滤波算法进行了化简,将滤波器的运算分解成简单的加、减、乘、除运算。利用硬件描述语言对Kalman滤波进行了FPGA实现,并对实现的滤波器进行了验证。通过软硬件仿真实验结果的对比,验证了所实现滤波器的有效性。     

Image reconstruction algorithm of electromagnetic tomography based on fractional Kalman filter

Aiming at the problem of low quality of image reconstruction of electromagnetic tomography (EMT), in this paper, an image reconstruction algorithm of EMT based on fractional Kalman filter (FKF) is proposed. Firstly, the principle of EMT and the principle of state equation of FKF are expound respectively. FKF is often used in the state estimation of nonlinear systems. There is a nonlinear relationship between the object field distribution and the sensor signal in the EMT. Therefore, according to this feature, FKF is applied to the image reconstruction algorithm of EMT. The image reconstruction process of EMT is regarded as the state estimation process of FKF, the normalized measurement voltage is taken as the observation value, and the sensitivity matrix is taken as the measurement matrix. To establish the nonlinear state estimation equation of the FKF and a priori estimation error covariance equation in the EMT, the gray value of image obtained by LBP is used as the initial value of the state estimation, a prior estimation state vector and a priori estimation error covariance matrix are obtained by prediction update, the Kalman filter gain and the posterior estimation error covariance matrix are obtained by the correction feedback process. After repeated iterations, the final state vector, i.e. reconstructed image of EMT is obtained. Finally, simulation experiments are carried out for seven different flow patterns. The results show that the image error and correlation coefficients of the reconstructed image of this algorithm are better than traditional algorithms such as LBP, Landweber, Kalman filter, and have better anti-noise effect than Kalman filter. Therefore, the image reconstruction algorithm of FKF is a new method and means to study the image reconstruction of EMT.     

Centroid weighted Kalman filter for visual object tracking

In the visual object tracking, the Kalman filter presents commonly the state model and observation model uncertainty in the actual performance of Gaussian noise, so it makes the estimation of certain parameters produce errors in the model, and results in decreasing estimation precision. In order to enhance the stability of the Kalman filter, an algorithm based on centroid weighted Kalman filter (CWKF) for object tracking is proposed in this paper. The algorithm firstly uses background subtraction method to detect moving target region, and then uses the Kalman filter to predict target position, combining centroid weighted method to optimize the predictive state value, finally updates observation data according to the corrected state value. Tracking experiments show that the algorithm can detect effectively moving objects and at the same time it can quickly and accurately track moving objects with good robustness.     

基于BP神经网络的Kalman滤波算法的改进

针对传统的Kalman滤波算法在机动较强的目标跟踪中误差变大甚至发散的缺点,考虑到BP神经网络具有较强的非线性逼近能力,提出了用BP神经网络辅助Kalman滤波的新算法,仿真表明该算法优于传统的Kalman滤波算法.     

Railway track geometry determination using adaptive Kalman filtering model

The Kalman filter has been widely used to solve different filtering problems especially in tracking and estimation applications. Besides its simplicity, robustness and optimality, the application of Kalman filter to nonlinear systems can be complicated. The most common method is to use extended Kalman filter which linearizes the nonlinear model so that the standard Kalman filter can be applied. In this paper, a new adaptive Kalman filtering algorithm is designed and applied to a railway track geometry surveying system which has been designed in the scope of a research project at Yildiz Technical University/Turkey. Track gauge, super-elevation, gradient and track axis coordinates which are the railway geometrical parameters can be instantly determined while making measurements by using adaptive Kalman filtering algorithm integrated surveying system.     

基于显微镜聚焦的微装配视觉伺服研究

利用显微镜聚焦理论,沿显微镜光轴方向移动物体,不断计算图像的灰度变化之和,可判断出物体沿光轴的坐标,将这一坐标集成在伺服控制方程中,可完成立体视觉跟踪。这样,采用单目视觉系统就可以获得物体的三维坐标,避免了双目立体视觉系统的复杂结构。为了提高系统图像处理速度,利用卡尔曼滤波器对跟踪的特征点进行预测,并用窗口处理技术减小图像处理区域。实验和仿真结果表明,上述方法可完成复杂微装配的视觉跟踪,系统有好的实时性。     

基于卡尔曼滤波的焊缝检测技术研究

提出一种基于卡尔曼滤波技术的电弧焊焊缝检测新方法。利用视觉传感器获取弧焊区熔池图像,并抽取图像质心作为描述焊缝位置的特征矢量,建立图像质心状态方程和测量方程。在有色噪声模型的基础上,应用卡尔曼滤波对图像质心位置和质心位移进行状态估计,得到最小均方差条件下的焊缝位置最佳预测值,从而减小过程噪声和测量噪声引起的焊缝位置测量偏差,实现弧焊过程中焊缝位置的精确检测。计算机仿真及实际焊接试验结果验证了该方法的有效性。     

An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs

Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known.     

A hybrid unscented filtering and particle swarm optimization technique for harmonic analysis of nonstationary signals

This paper presents a modified unscented Kalman filter for accurate estimation of frequency and harmonic components of a time-varying signal embedded in noise with low signal-to-noise ratio. Further, the model and measurement error covariances along with the unscented Kalman filter parameters are selected using a modified particle swarm optimization algorithm. To circumvent the problem of premature convergence and local minima, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity and better accuracy of the unscented Kalman filter parameters. Various simulation results for nonstationary sinusoidal signals with time varying amplitude, phase and harmonic content corrupted with noise, reveal significant improvement in noise rejection and speed of convergence and accuracy in comparison to the well known extended Kalman filter.