SPH

matlab大全，专门用于图像处理和图像获取(matlab codes for graph management)

说明：  S变换的MATLAB实现程序。写的很具体。(S transform)

一般支持向量机的回归和分类程序，均可以运行(General support vector machine regression and classification procedures can run)

在运载体运动情况下,提出了一种适用于捷联惯导系统(SINS)初始对准的新算法,该算法以惯性空 间为过渡参考基准,即建立了初始时刻惯性坐标系(i0系)和初始时刻捷联惯组惯性坐标系(ib0系)两个惯性坐标 系。将捷联惯组坐标系(b系)与导航坐标系(n系)之间的初始对准姿态矩阵的实现分解为三步骤:(1)通过地理 位置和初始对准时间求解i0系至n系的变换矩阵 (2)通过惯导比力方程变形,引入测速仪速度辅助,求解从ib0 系至i0系的常值变换矩阵 (3)使用姿态更新算法实时计算b相对于ib0系的变换矩阵。最后,载车运动环境下的 初始对准试验结果表明航向角对准精度达到了0.1b(1R)。 (On the moving base, a new initial alignment algorithm for strapdown inertial navigation system (SINS) is proposed. In this algorithm, two special inertial frames, the initia-l time inertial frame (i0frame) and the initia-l time SINS body inertial frame (ib0frame), are defined and are selected as transition reference frames. The calculation of initial alignment direction cosine matrix (DCM) between SINS body frame (bframe) and nav- igation reference frame (nframe) is expanded into three parts: (1) the DCM betweeni0frame andnframe is easily obtained via local geographical coordinates and initial alignment time (2) by expressing SINS specific force equation inbframe and by the aid of external velocity log, the constant DCM fromib0frame toi0frame is achieved (3) attitude updating using gyro samples gets the DCM betweenbframe andib0frame. In the end, some vehicular tests are carried out on the moving base and the accuracy of azimuth reaches about 0.1b(1R). )

关于matlab计算与仿真的ppt,关于matlab计算与仿真的ppt(a ppt about the simulation and computation of matlab)

对输入的图像实现高斯金字塔分解，最后返回一个储存各层图像矩阵的元胞。(Gaussian Pyramid decomposion for image)

求解常微分方程组的bvp解算器的使用说明和教程和实例(tutorial and documentary and examples for ODE bvp)

说明：  室内可见光通信系统信道模型，用于室内可见光通信系统基础信道估计仿真实验(Channel Model of Indoor Visible Communication System for Basic Channel Estimation of Indoor Visible Communication System)

贝叶斯估计 和极大似然估计的matlab程序(matlab program of Bayesian estimation and maximum likelihood estimation )

这是关于vmd变分模态分解的matlab代码，主要应用于信号的分解，数据的处理等。(This is the matlab code for VMD variational mode decomposition, which is mainly applied to signal decomposition, data processing and so on.)