GA_P_Median

精通matlab6.5的源代码，很好的一本 学习matlab的书(Matlab6.5 proficient in the source code, a good book to learn matlab)

matlab 蚁群算法 源程序，里面有多个m文件(ant colony algorithm matlab source code, there are several m-files)

particle filtering RBPF in matlab

it is most usefull project for students which are the programmers

it s flight vizualisation matlab

这些程序都是自己写作业时留的，希望能对大家有用处。 ( )

说明：  经典滤波器设计，设置高通、低通、带通滤波器(Classic filter design, set high-pass, low pass, band pass filter)

(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.)

利用扩展卡尔曼进行状态估计 状态向量四维 测量向量二维 误差为高斯白噪声(The use of extended Kalman state vector for state estimation of two-dimensional four-dimensional vector measurement error is Gaussian white noise)

Edge detection result should be enhanced using linear method like Median filter to remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the centre of the pupil by counting the number of black pixels (zero value) of each column and row. Then get each row and column that has the maximum number of these black pixels. Then determine the center by simple calculation according to the image coordinate to set it correct on the image, consequently we can determine the radius of the pupil. Thus we can find the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms of circle and ellipse.