GrTheory

Tactial+and+Strategic+Missile+Guidance一书程序源码（For Mac in Matlab）(Tactial+and+Strategic+Missile+Guidance book program source code (For Mac in Matlab))

本压缩包包含一个ppt文件，主要介绍关于MATLAB的用法及入门介绍(This archive contains a ppt file, describes the usage and on introduction to MATLAB)

这是一个延时求和波束形成算法的实现，可以运行！(This is a summation delay beamforming algorithm, you can run!)

变压器matlab仿真，用于实现基本的变压器操作和应用(Transformers matlab simulation, used to implement the basic operation and application of transformer)

自适应滤波变步长Lms归一化算法的仿真实现程序(simulation in lms normolized algorithm)

说明：  这是国外一本经典教材附带的程序，通信系统的matlab实现，所含例子丰富(This is a classic foreign materials attached to the procedures, communications systems to achieve matlab contains abundant examples of)

这是一本研究目标定位跟踪的学习手册（在雷达，声纳，无线传感器网络领域等），采用的方法有最小二乘/极大似然，卡尔曼滤波，蒙特卡洛，粒子滤波等方法，更是很多读者需要的是里面有大量的仿真实例和程序代码，并配以中文注释，读者可以结合书中的原理和公式，对程序有进一步的理解。也适合很多读者在已有的代码的基础上对算法进行改进。(This is a study targeting the learning manual tracking (radar, sonar, wireless sensor networks, etc.), using the least squares method/maximum likelihood, Kalman filtering, Monte Carlo, particle filtering methods , but many readers need is there are a large number of simulation examples and program code, and with Chinese notes, the book the reader can combine the principles and formulas, a better understanding of the program. Also suitable for many readers on the basis of the existing code to improve the algorithm.)

test file for matlab kalman filter

％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)

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