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target tracking in wsn

这是“受控自回滑动平均模型”CARMA非线性系统的随机梯度辨识算法，用于参数估计以及模型建模。(This is a " controlled slide back the average model" CARMA nonlinear system stochastic gradient identification algorithm for parameter estimation and modeling.)

说明：  《Matlab从入门到精通》所有的源程序。(《Matlab from entry to the master》 all of the source.)

这是一个快速傅里叶变换分析谐波类程序，以及生成火柴梗图的程序(This is a class of fast Fourier transform analysis of harmonic procedures, and the program generates Stick Figure)

说明：  5种不同类型的PCB可以用这个工具实现互相转换(5 different types of PCB can use this tool to achieve mutual conversion)

用MATLAB编写的非近轴近似的高斯光束，可以更精确地描述高斯光束。(MATLAB prepared with a non-paraxial approximation of the Gaussian beam can be more accurately describe the Gaussian beam.)

A PIV Post-processing and data analysis toolbox(The PIVMat Toolbox for Matlab contains a set of command-line functions to import, post-process and analyse 2- and 3-components vector fields from PIV (particle image velocimetry), stereo-PIV, DIC (digital image correlation) SS (synthetic schlieren) or BOS (background-oriented schlieren) applications. Main Features: - Import vector fields from PIV (Particle Image Velocimetry) or other related technics, such as DIC (Digital Image Correlation), SS and BOS (Synthetic Schlieren and Background-oriented Schlieren). - Compatibale with files from DaVis (LaVision), DPIVSoft (Meunier & Leweke), VidPIV (Oxford Laser), MatPIV and Optical Flow. - Standard vector field operations: interpolation, filtering (median, Butterworth...), averaging (temporal, spatial, azimuthal...), derivatives computation (vorticity, divergence, strain, Q-factor...) - Fully vectorized: all operations directly apply on arrays of fields (no for loops) - More than 60 functions with full on-line documentation sampl)

用matlab写的图像骨架提取，可以完整运行。。。。(extract skeleton based on Matlab.........................)

说明：  这是一个地震波正演的程序，采用雷克子波，可以画出地震的变面积图(use ricker wavelet to do the forward of the seismic)

卡尔曼滤波器，用于平滑惯性器件的输出数据，从而可以对数据进行建模与补偿。 (Kalman filter, inertial device for smoothing the output data, allowing for data modeling and compensation.)