nedi

数据关联是多目标跟踪的一项关键技术。JPDA是大家公认的多目标跟踪中性能较好的数据关联算法，它 认为量测和目标是一一对应的关联关系，但在许多实际情况中，量测和目标是多一多对应的关系。针对上述情况，该文提 出了广义概率数据关联算法(Generalized Probability Data Association，GPDA)。文中从理论上对这两种算法的性能进行了 详细分析，并利用Monte Carlo技术对其性能进行了仿真比较。(Data association is one of the key technologies in multi—target tracking．And JPDA is considered as the best da· ta association method．JPDA considers the association of measurements with targets is simply one-to-one problem．But in many practical cases，the association of measurements with targets will be multiple—to—multiple problem．For this case，a Generalized Probability Data Association(GPDA)algorithm is proposed in this paper．Furthermore，this paper analyzes the performance of these two algorithms theoretically．And we give the comparative analysis of those performances by using Monte Carlo method．)

基于粗糙集的特征子集筛选的一种算法 ！！！(Based on rough set feature subset selection of an algorithm! ! !)

Several Simulink tutorials that I have collected.

高斯消元法MATLAB实现，小报告形式展现(Gaussian elimination MATLAB implementation)

使用遗传算法对BP网络权值阈值进行优化，再用BP算法训练网络的代码，文本格式的(bp propoganda and genetic algorithm)

说明：  C-means一个简单完整的程序，供大家看一下，有不完善的地方，请多多指教(C-means a simple and complete procedures for Members to see, any problems, please exhibitions)

包括图像分析的四部分代码：matlab扩散和高斯函数，线性扩散，线性复扩散，非线性扩散。(It contains four parts： [1]MATLAB function:diffusion.m gauss.m [2]Linear diffusion Applying linear diffusion to images creating linear scale-space. MATLAB code: demo_lin.m Image: haifa1.bmp [3]Linear complex diffusion Applying linear complex diffusion creating Gaussian and Laplacian scale-spaces. MATLAB code: demo_cmplin.m [4]Nonlinear diffusions: Perona-Malik ["Anisotropic diffusion"] Catte et al. regularization of P-M Complex ramp-preserving diffusion Nonlinear edge preserving diffusions.The classical Perona-Malik process: the value of the diffusion coefficient is reduced near edges estimated by the first derivative. Best applied to step edges. A regularized version by Catte et al- the gradient estimation for controlling the process is smoothed by a Gaussian. Proved to be mathematically well-posed. Ramp preserving complex diffusion- best for ramp-type edges. Results are smoother with almost no staircasing effects. MATLAB code: demo_nldif.m Image: ct_scan.bmp)

说明：  怎么在函数中加入高斯噪声，白噪声，比较它们的性质，并求它们的相关性(how to insert gaosi noise,white noise and then how to konw their correlation)

MPEG-1 Layer-3 simulation

OFDM simulation for transmission and reciever