gpml-demo

基于KL变换的目标识别技术，个人认为效果比神经网络要好(KL transform based target identification technology, think better results than the neural network)

说明：  张量代数，张量分析以及张量应用的简单介绍(tensor )

FHSS_DSSS_CDMA的m文件，可以在matlab的simulink上仿真~(FHSS_DSSS_CDMA the m file )

基于GUI的计算器实现了基本加减乘除的计算机的功能，可以matlab仿真。(It is a good GUI.)

基于均匀线阵，用music对信号进行多频率估计(frequece of estimaton )

这是一个关于多目标数据融合的很好的温章，希望对大家有所帮助。原理清晰，见解独特(This is a multi-target data fusion on a good article you want to help. Principle of clear, maverick)

its the data base loading file in matlab

自己编写的稀疏阵列的matlab波束形成程序，可画出9阵元稀疏阵列的阵列方向图，以及相应阵列的排列示意图和同等数目阵元的均匀阵列的对比效果图(sparse array beamforming)

说明：  粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 PSO模拟社会采用了以下三条简单规则对粒子个体进行操作：①飞离最近的个体，以避免碰撞。②飞向目标。③飞向群体的中心。这是粒子群算法的基本概念之一。 粒子群算法其基本思想是受许多鸟类的群体行为进行建模与仿真研究结果的启发(Particle swarm optimization (PSO) is a population based stochastic optimization techniques. Based on evolutionary algorithms compared with other groups, they are initialized to a random solution, iterative search through optimal solution. The difference is: the principle of survival of the fittest evolutionary computation to follow, while PSO simulation community. The potential of each solution expressed as a group of particles, each particle has its own position vector and the velocity vector, and a fitness determined by the objective function. All particles in the search space at a constant speed flight, by following the current search to find the optimal values ​ ​ of the global optimum. PSO simulation community has adopted the following three simple rules for the operation of individual particles: ① recently departed individuals, in order to avoid collisions. ② to the target. ③ fly to center groups. This is one of the basic concepts of particle swarm algorithm. PSO algori)

说明：  关于非高斯噪声环境下的各种调制方式信道容量仿真(Non-Gaussian noise environment on a variety of channel capacity modulation simulation)