dbf_mvdr

various code .................................

关于飞行器制导控制方面比较全面的仿真源代码，其中包括飞行器三自由度和六自由度仿真，单次仿真和蒙特卡洛仿真，EKF算法和UKF算法仿真等等。(On aircraft guidance control more comprehensive simulation source code, including aircraft and six degrees of freedom simulation of three degrees of freedom, a single simulation and Monte Carlo simulation, EKF and UKF algorithm simulation algorithms and so on.)

PSO algorithm to tuning PID

Iterative Closest Point

用MATLAB写的ekfukf工具包。绝对高质量代码。很方便拓展。在控制，估计领域应用广泛！(Written ekfukf using MATLAB tool kit. Absolute quality code. Easy to expand. )

parser program for compiler design

图书电扫阵列-matlab建模与仿真，第三章编码(codes of book chapter3)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)

说明：  很好用的小波算法，具有良好的尺度不变形，对初学者有用。(Nice wavelet algorithm, have good deformation, the scale of beginners useful)

说明：  改进型K分布还杂波程序，非常棒的MATLAB源码呵呵，谁用谁知道！(K distribution clutter also improved procedures for the MATLAB source Oh great, who is with who knows!)