statcom_gto48p

Dynamic control of the 6WD skid-steering mobile robot RobuROC6 using sliding mode technique

Face Detection System training function

卡尔曼滤波在雷达目标跟踪上的仿真,设置了一个仿真场景，然后进行仿真以及分析。(Kalman filter simulation on the radar target tracking)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

真对gpr在不同介质层的模拟，采用matlab编程形式，得到各种波形。(simulation of ground penetrating radar in different mediums.)

多传感器阵列信号的分布式恒虚警检测 广义似然比(Multi-sensor array signal distributed CFAR detector)

the basic code for mimo using 2x2 antenna

eige value problems in math

基于卡尔曼滤波技术的永磁同步电机故障诊断与容错控制源码，文章已经发表了，收录于ICACMVE‘07会议论文(Kalman filtering technique based on permanent magnet synchronous motor fault diagnosis and fault-tolerant control source, the article has published a collection of conference papers in ICACMVE)

path planning by potential field method