QAM-performance-with-AGC-errors

matlab 的s函数模板，用s函数是，在这个基础上修改后就可以用了！！(matlab s-function template, use the s function is, on this basis can be modified after it! !)

关于系统辨识的一些仿真程序，里面包括各种经典算法(On system identification of a number of simulation procedures, which include a variety of classic algorithms)

求取系统特征模态,以及参数辨识使用。采用扩展的prony算法。(eigen value dentification)

近距离卫星的任务规划。论文中给出了详细的程序设计和Basic以及Matlab程序代码。(Given an initial orbit and a set of other orbits of interest, the path requiring the lowest change in velocity between them is of high value. Software was developed to visualize the change in velocity required from a user defined window of burn to a user defined window of intercept. The time between burn and intercept is unrestricted. Multiple intercepts require searching an n! solution space. Research then is focused on ways to pick optimal choices without fully calculating out the cost of doing all possible combinations. Some code was translated from BASIC and the rest was newly developed in MATLAB. The code was validated using orbits with known intercept solutions.)

kalman filter design for windowing radar target detection

说明：  字符从文章的最顶端掉下来的特效显示效果 (Characters from the top of the article fall display of special effects)

二维MUSIC算法，可用于空间定位，AOA和TOF(Two-dimensional MUSIC algorithm, which can be used for spatial positioning, AOA and TOF)

EMD的边际谱的算例，很实用，做这一块的快下(EMD example the marginal spectrum, it is practical to do this under a piece of the fast)

感知器感知器算法训练二元线性分类器。结构体数据使用感知器学习规则 找到给定的二分类标签数据超平面。 (PERCEPTRON Perceptron algorithm to train binary linear classifier. Synopsis: model = perceptron(data) model = perceptron(data,options) model = perceptron(data,options,init_model) Description: model = perceptron(data) uses the Perceptron learning rule to find separating hyperplane from given binary labeled data. model = perceptron(data,options) specifies stopping condition of the algorithm in structure options: .tmax [1x1]... maximal number of iterations. If tmax==-1 then it only returns index (model.last_update) of data vector which should be used by the algorithm for updating the linear rule in the next iteration. model = perceptron(data,options,init_model) specifies initial model which must contain: .W [dim x 1] ... normal vector. .b [1x1] ... bias of hyperplane. .t [1x1] (optional) ... iteration number. Input: data [struct] Labeled (binary) training data. .X [dim x num)

INterval analisis Optimization constrained