RobustAdaptiveControl3

HOG特征的实现.INRIA cvpr2005论文实现(HOG feature implementation. INRIA cvpr2005 paper to achieve)

msk调制，希望大家好好学习，天天向上，努力提高自己(msk modulation, I hope you study hard, every day, strive to improve their)

卡尔曼滤波器处理有色噪声和有色噪声的白色化(Kalman filter processing colored white noise and colored noise of)

GPS单点定位程序，包含RINEX文件 .N和.O文件，得到观测点位置(GPS point positioning procedures, including RINEX files .N and .O files get observation point)

读取视频文件的函数。比matlab工具箱中的mmreader更好用。(Read the video file functions. Than matlab toolbox mmreader better use.)

this is explains overlap save and add method in matlab

(不错的一篇文章，已被EI收录)模型预测控制（model predictive control，MPC）路径规划算法适用于三维动态环境下的无人机（un-manned aerial vehicle，UAV）路径规划；动态贝叶斯网络（dynamic Bayesian network，DBN）能够有效推理战场态势，对无人机进行威胁评估。针对威胁尾随无人机时的路径规划问题，构建 DBN 威胁评估模型，将 UAV 在战场环境中的威胁态势用威胁等级概率表示，与 MPC 路径规划算法相结合，得到基于 DBN 威胁评估的 MPC UAV 路径规划算法。通过多组仿真分析表明，在三维动态环境下，特别是威胁尾随无人机时，基于 DBN 威胁评估的 MPC无人机路径规划算法可以得到有效的无人机路径。(The model predictive control (MPC)path planning algorithm can solve the problem of dynamic unmanned aerial vehicle (UAV)path planning.Dynamic Bayesian network (DBN)is an effective tool for rea-soning and threat assessment.Considering the problem of path planning when the dynamic threat tags the UAV,the MPC path planning algorithm combined with DBN threat assessment is presented,which used the threat lever probability to describe the threat situation.A group of simulations demonstrate the efficiency of MPC three-dimensional dynamic path planning algorithm for UAV based on DBN threat assessment especially when the dynamic threat tags the UAV.)

MATLAB Image Detection

混沌系统的李亚普洛夫指数的求解，步骤很详细，可以适用于不同的混沌系统。(Chaotic system Lyapulov Index solving steps in detail, can be applied to different chaotic systems.)

The following fuzzy modeling exercises run in the MATLAB environment. The interaction required by the user is guided by instructions and editable fields menus. The user can change various parameters of a system, and execute modeling commands to verify the resulting system. In some exercises, it is also possible to manually edit the fuzzy system initially generated with the parameters given by the user, in order to enhance and fine tune the performance of the model.