matlab实现wcdma的仿真程序

MATLAB的三维重建程序，很好用的，其中有两个函数(MATLAB three-dimensional reconstruction process, a very good use, there are two functions)

维纳滤波器的matlab实现外加实验报告，有程序(Wiener filter matlab plus the realization of the experimental reports, there are procedures)

nioton-rafson matlab program

利用MATLAB实现的一个简单的水声波束形成程序。实现波束入射角度的定位功能。(The use of MATLAB to achieve a simple acoustic beam-forming process. To achieve the positioning function of beam incident angle.)

说明：  auv滑模平移运动控制器及其simulink实现(AUV sliding mode translational motion controller and its Simulink implementation)

说明：  这是本人在做硕士论文时开发 的有关GUI 图象设计的代码(this is my master's thesis done at the development of the graphic design of the GUI code)

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)

此程序为基于CAR的递推最小二乘算法，使用语系统辨识的初学者(This procedure is based on the CAR of the recursive least squares algorithm, using the language of system identification for beginners)

经典的MUSIC算法，很适合新手的学习和认识(Traditional MUSIC ，DOA adaptive array suitable for newer learning)

Learn Kalman Filter( Kalman Filter)