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Search Results: 1 - 10 of 19974 matches for " 模糊Kalman滤波 "
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基于模糊自适应Kalman滤波的GPS/DR数据融合
唐磊,赵春霞,唐振民,成伟明,张浩峰
控制理论与应用 , 2007, DOI: 10.7641/j.issn.1000-8152.2007.6.005
Abstract: 针对标准Kalman滤波器对系统模型依赖性强、鲁棒性差,而GPS/DR系统的准确数学模型难以建立的问题,提出了一种模糊自适应联邦卡尔曼滤波器(FAFKF).首先通过模糊自适应滤波控制器监控观测量的残差理论值和实际值,并通过实时增强它们的一致性来调整各子系统观测噪声方差阵,使之更符合真实的模型,有效提高了Kalman滤波器对模型变化的适应能力.然后通过模糊自适应信息融合控制器对各子系统可信度进行模糊评判,并根据可信度自适应地计算信息分配系数来实现数据的融合.理论分析和实验数据表明该滤波器在滤波精度、容错性能上都有了很大的提高.
一种基于卡尔曼滤波和模糊控制的rbf神经网络新型学习算法
王君,蔡之华,朱莉
计算机应用 , 2006,
Abstract: ?提出了基于kalman滤波最优估计和模糊控制的径向基函数(radicalbasisfunction,rbf)神经网络学习算法,用实例进行了仿真实验。结果表明,与传统的rbf网络学习算法比较,该算法具有明显快速的学习效率和较高的识别精度.
A novel learning algorithm for RBF network based on Kalman filter and fuzzy control
一种基于卡尔曼滤波和模糊控制的RBF神经网络新型学习算法

WANG Jun,ZHU Li,CAI Zhi-hua,
王君
,蔡之华,朱莉

计算机应用 , 2006,
Abstract: A novel training algorithm for RBF(Radical Basis Function) networks was proposed based on extended Kalman filter and fuzzy control.The simulation results were presented on RBF networks as applied to the Iris classification problem.It is shown that the use of the extended Kalman filter and fuzzy control results in faster learning and higher precision of identification than conventional RBF networks.
RESEARCH ON LONG BASELINE AMBIGUITY RESOLUTION OF NETWORK RTK
网络RTK长基线模糊度解算方法研究

Ke Fuyang,Wang Qing,and Pan Shuguo,
柯福阳
,王 庆,潘树国

大地测量与地球动力学 , 2012,
Abstract: 基于部分模糊度和Kalman滤波提出了一种适于长基线的模糊度解算方法。该方法首先由两步法和部分模糊度方法解算出高高度角卫星的模糊度;然后由该部分模糊度采用Kalman滤波估计出参考站天顶的大气延迟误差;最后利用估计的天顶大气误差对长基线低高度角卫星的模糊度进行改正,以获得准确值。利用江苏CORS部分基准站数据对该方法进行验证,结果表明该方法固定的长基线、低高度角卫星双差模糊度是准确、可靠的。
Control algorithm and application of a new fuzzy-Kalman filter
一种新的模糊-卡尔曼滤波器的控制算法及应用

ZHANG Gao-yu,ZHAO Heng,YANG Wan-hai,
张高煜
,赵恒,杨万海

控制理论与应用 , 2005,
Abstract: A new fuzzy-Kalman filter is presented to meet the requirement of accurate and quick prediction of the tube's movement in high speed cutting machine.Two fuzzy surfaces are used to represent respectively the relation between process noise and the mean and covariance of residual,as well as the relation between measurement noise and the mean and covariance of residual,which makes the reasoning of fuzzy rule unnecessary.The relation between the filter sampling time and cutting error is fuzzed.The capability of tracking and the prediction of cutting error are improved by the filter with this structure.The simulation shows that the new fuzzy-Kalman filter has advantages over those available methods in higher speed and better stability in reducing cutting error and improving precision.
一种基于模糊阈值的直线拟合策略在铁路沿线柱体检测及距离估计中的应用
李赣华,刘云辉,蔡宣平
中国图象图形学报 , 2006, DOI: 10.11834/jig.200608190
Abstract: 首先提出了一种单幅图像中由边缘检测自动估计铁路沿线柱体到铁轨大致距离的方法,该方法主要通过对铁轨及其沿线柱体的检测、分类识别和距离估计来完成。因为如何在有畸变和复杂背景的图像中准确和有效检测边缘在图像处理和模式识别中一直是一个关键而困难的问题,为此提出了一种基于模糊阈值的直线连接拟合策略,该策略主要包括3个步骤:边缘提取、角点检测和基于模糊阈值的直线递归拟合。该策略可以有效地在有图像畸变和复杂背景的实际拍摄图片中通过参数控制获取感兴趣目标的直线边界。实验结果证明本文的直线拟合策略是精确的和具有鲁棒性的,距离估计方法是有效的。
A Video Stabilization Algorithm Based on Feature Tracking
一种基于特征跟踪的视频序列稳像算法

史阳,高新波
光子学报 , 2005,
Abstract: A video stabilization algorithm is presented based on the feature window tracking. First, the proposed algorithm extracts the corner-points as feature from the specified reference frame. Then, based on the fuzzy Kalman filtering, the extracted features are tracked with window-matching method from the succeed frames. By registering the corresponding feature windows, the motion parameters of the camera can be calculated between the reference frame and succeed frames. Finally, vibration of images due to camera shake is stabilized with motion compensation. The experimental results illustrate that the proposed algorithm is effective and robust with low computational complexity.
GPS/DR data fusion based on fuzzy adaptive Kalman filter
基于模糊自适应Kalman滤波的GPS/DR数据融合

TANG Lei,ZHAO Chun-xi,TANG Zhen-min,CHENGWei-ming,ZHANG Hao-feng,
唐磊
,赵春霞,唐振民,成伟明,张浩峰

控制理论与应用 , 2007,
Abstract: Standard Kalman filter strongly depends on the system model. Unfortunately, an accurate mathematical model of GPS(global positioning system)/DR(dead reckoning) system is difficult to set up, so a fuzzy adaptive federated Kalman filter (FAFKF) is presented for the problem. First, a real-time fuzzy adaptive filter controller is used to monitor the real value and theoretical value of residual covariance, and adjust the covariance matrices of observation noises towards the real model by enhancing their consistencies. As a result, the Kalman filter's tolerance to model error is improved. Then a fuzzy adaptive data fusion controller is used to evaluate the reliability of each subsystem, and the information distribution coefficients of each subsystem is computed according to the reliability. Theoretical analysis and experimental data show that both the precision and fault tolerance of FAFKF are improved.
Prediction of chaotic time series based on fuzzy model
基于模糊模型的混沌时间序列预测

Wang Hong-Wei,Ma Guang-Fu,
王宏伟
,马广富

物理学报 , 2004,
Abstract: For dynamic systems with complex, ill-conditioned, or nonlinear characteristics, the fuzzy model based on fuzzy sets is very useful to describe the properties of the dynamic systems using fuzzy inference rules. Modeling and prediction of nonlinear systems using fuzzy modeling is discussed in this paper. First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm. To illustrate the performance of the proposed method, simulations on the chaotic Mackey-Glass time series prediction are performed. Combining either off-line or on-line learning with the proposed method, we can show that the chaotic Mackey-Glass time series are accurately predicted, and demonstrate the effectiveness.
基于运动约束的脉冲雷达游标测距方法
Vernier ranging method for pulse radar based on motion constraints

陈浩,郭军海,齐巍
- , 2015, DOI: 10.13700/j.bh.1001-5965.2014.0095
Abstract: 摘要 为解决传统脉冲雷达游标测距中解相位模糊和解速度模糊相互耦合的问题,将目标的运动约束与传统游标测距结合在一起,提出了一种新的基于运动约束的游标测距方法.利用运动约束积累一段时间的观测数据进行UKF滤波,得到精度较高的径向速度来解速度模糊,得到的无模糊速度可用于距离游标.利用得到的游标距离取代脉冲测距数据进行UKF预测,可准确估计下一时刻的速度并解速度模糊,这样建立了可同时解相位模糊和解速度模糊的耦合滤波器,成功实现脉冲雷达游标测距,并大大减小脉冲雷达测距随机误差.高速飞行器主动段仿真和脉冲雷达实测数据验证表明,该算法能大大减小径向距离随机误差,将距离随机误差减少一个数量级至分米级.
Abstract: Traditional pulse radar Vernier ranging method has the problem of coupled ambiguity in resolving the ambiguity of Doppler phase and velocity. To solve this problem, the motion constraint of target was applied into Vernier ranging and a new motion constraint Vernier ranging method was proposed. Accumulating a period of measurement data, an unscented Kalman filter was used to estimate radial velocities with higher accuracy. The high accuracy velocities were used to resolve the velocity ambiguity to startup the Vernier ranging. The radial velocity of next time period was accurately estimated through UKF forecast on the Vernier range, and the velocity ambiguity of next period was resolved. The coupling filter that could resolve phase ambiguity and velocity ambiguity was built, and the random errors of pulse radar radial range data were greatly eliminated using this new Vernier ranging method. Simulations of high-speed aircraft in boost phase and measured data of pulse radar prove that, this motion constraint Vernier ranging method greatly reduces the random error of radial range from meters to decimeters.
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