control-system

机动目标的跟踪问题一直是人们研究的重点，实现机动目标精确跟踪，首要解决的问题就是使所建立的目标运动模型与实际的目标运动模型匹配。目前常用的有多模型(MM)，交互式多模型(IMM)，切换模型等。多模型方法就是对一组具有不同机动模型分别进行Kalman滤波，最终的参数估计是各滤波器估计值的加权和；在多模型基础上，Shalom提出了交互式多模型方法，这一方法对无序目标的机动检测，显示了更好的鲁棒性和跟踪的稳定性；切换模型则是分别建立机动和非机动运动模型，利用机动检测实现在这两个模型之间的切换。一般来说，交互式多模型的跟踪性能较好。(mobile target tracking problem has been a focus of research and achieve precise maneuvering target tracking, the most pressing issue is to enable the establishment of the target model and the actual movement of the target model matching. At present, more commonly used model (MM), interactive multi-model (IMM), switching model. Multi-model approach is a group of different mobile models were Kalman filtering, The final parameter is the estimated value of the filter is estimated weighted; in the multi-model basis, Shalom made the interactive multi-model approach that the objectives of the motor disorder detection, better show the robustness and stability of the track; switching model is set up motorized and non-motorized sports model, the use of mobile testing to achieve in this model between)

support vector machine （支持向量机）方法是目前分类方法中比较好的一个分类方法，实验证明准确度非常高！(support vector machine (support vector machine) method is a better classification of a classification method, experimental proof of a very high accuracy!)

Nonuniform mesh fullvector : Shows how to use the full-vector modesolver with a nonuniform mesh.

关于目标追踪的一个源代码，比较了卡尔曼滤波和粒子子滤波的跟踪效果。 ，经测试可直接使用。 (A source code on the target track, and compare the tracking performance of the Kalman filter and particle sub-filter. Has been tested and can be used directly.)

matlab高编程书籍，实用性强，属于提高类书籍(High matlab programming books, practical)

Generalized Givens Transformation and its Application

GAUSSIAN FUNCTION IMPLETEMENTD IN MATLAB BUT I NO SURE THIS WORK

正交相位调制QPSK的数字调制于解调的matlab实现(Digital quadrature phase modulation QPSK modulation in demodulation matlab realization)

说明：  matlab开发的RBF、BP PID算法，已经过测试(matlab development of RBF, BP PID algorithm has been tested)

以为高斯和密度估计，使用高斯核的非参数密度估计方法，对样本进行概率密度估计，程序中给出了窗宽的估算公式。(That the Gaussian and density estimation, using Gaussian kernel non-parametric density estimation method, the sample probability density estimates, the program gives the formula for bandwidth estimation.)