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基于“当前”统计模型的模糊自适应跟踪算法
我存的一篇论文，拿来与大家共享-Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets.So it
may be difficult to meet all maneuvering conditions.The Fuzzy inference combined with Current stati
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结合雷达跟踪的空中目标的实际情况，针对目标运动模型中的线性运动和非线性运动，分别设计了两种模型，并用马尔科夫状态转移矩阵实现IMM算法。最后对交互多模型卡尔曼滤波算法进行了Matlab仿真及结果分析。-Combined with radar tracking air targets, for linear and non-linear movement of target motion model, two models were designed and used a Markov state
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用MATLAB实现的卡尔曼滤波，根据有用信号和干扰噪声的统计特性（自相关函数或功率谱），以线性最小均方误差（MSE），能最大程度的滤除干扰噪声，提取有用信号。-Kalman filter using MATLAB, based on the statistical properties of the useful signal and interference noise (autocorrelation function or power spectrum), linear minimum m
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设一个系统的真实值为余弦函数，加入的系统过程噪声和测量噪声都满足正态分布，状态方程和量测方程都是线性的，用卡尔曼滤波求出最优结果-Set up a system of real value cosine function, the process of adding system noise and measurement noise are normally distributed, the state equation and measurement equation is linear,
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this file contains 4 parts:
1- sherlock C code for plagiarism detection in text document
2-linear kalman filter in simulink
3-sfclfunc m file for Superconducting fault current limiter
4-lightning protection is GUI matlab file for lightning pr
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matlab卡尔曼滤波文件，实现线性和非线性卡尔曼滤波，内容丰富实用-matlab Kalman filter files to achieve linear and nonlinear Kalman filtering, content-rich and practical
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输入为九轴数据，运用线性卡尔曼滤波，模拟出物体运动轨迹-For the nine-axis data input using the linear Kalman filter to simulate the trajectory of moving objects
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卡尔曼滤波（Kalman filtering）一种利用线性系统状态方程，通过系统输入输出观测数据，对系统状态进行最优估计的算法。由于观测数据中包括系统中的噪声和干扰的影响，所以最优估计也可看作是滤波过程。(Calman filter (Kalman filtering) uses the state equation of linear system and optimally estimates the state of the system by input and output obser
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针对线性高斯系统，采用共轭分布实现量测噪声未知情况下的状态估计和噪声方差辨识。(For linear Gaussian system, a conjugate distribution is adopted to estimate system state and identificate measurement noise variance.)
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基本卡尔曼滤波，python实现。 目标跟踪，物体匀速运动。(basic linear kalman filter, implemented by python.)
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一个关于姿态的卡尔曼滤波器用着非常不错大家都来用用(A linear Kalman filter estimating altitude and vertical velocity by doing sensor fusion of acceleration and any altitude measurement such as e.g. barometer or SONAR.)
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