CRCencode

说明：  matlab的雷达相关程序，包括MTIMTDCFAR，动目标自适应处理(Matlab radar procedures, including MTIMTDCFAR, Moving Target Adaptive Processing)

利用相关角度根据对应的转换矩阵完成惯性坐标系到载机坐标系的转换(Coordinate conversion)

利用mean5—3平滑对曲线进行拟合，可用于摩擦系数曲线，非线性数据进行拟合(Fit the curve with mean5-3 smoothing, which can be used for friction coefficient curve and nonlinear data for fitting)

OFDM系统中提出一种改进方法对频偏的捕获, 在联合使用9个短前导序列和长前导序列估计算法的基础上,采用鉴频环路的方法来消除算法中的除法和取模运算 (OFDM system in an improved way of deviation of the capture, in the joint use of nine short leader sequence and the long preamble sequence estimation algorithm, based on the frequency discriminator method to eliminate the loop algorithm in the division and modulo operation)

粒子群优化算法 ，应该直接就可运行，适合初学(particle swarm optimization)

一些个 人收集的小波变换的程序，仅供学习参考，没有什么重要意义(Some of my personal collection of wavelet transform procedures for reference purposes only study, nothing of great significance)

v(k)随机信号，输入信号为幅值为M序列程序.利用极大似然递推进行参数辨识，文件的*m文件，可以直接运行，能直接辨识出参数，绘制误差曲线等，程序中带有详细注释。(v (k) random signal, the input signal amplitude for the M series for the process. the use of recursive maximum likelihood parameter identification, documentation* m files, you can directly run directly identified parameters, such as mapping error curve, procedures with detailed notes in.)

说明：  matlab图像分割的实例，包括基于L*a*b空间的彩色图像分割，检测汽车目标，分水岭分割等程序源代码(matlab examples of segmentation, including those based on L* a* b color space, image segmentation, target detection vehicles, watershed segmentation source code)

Matlab Filter Implementation In this section, we will implement (in matlab) the simplest lowpass filter $displaystyle y(n) = x(n) + x(n-1), n=1,2,ldots,N $ (from Eq.$ ,$(1.1)). For the simplest lowpass filter, we had two program listings: Fig.1.3 listed simplp for filtering one block of data, and Fig.1.4 listed a main program for testing simplp. In matlab, there is a built-in function called filter3.3 which will implement simplp as a special case. The syntax is y = filter (B, A, x) where x is the input signal (a vector of any length), y is the output signal (returned equal in length to x), A is a vector of filter feedback coefficients, and B is a vector of filter feedforward coefficients. The filter function performs the following iteration over the elements of x to implement any causal, finite-order, linear, time-invariant digital filter:3.4

用sobol方法计算基于方差的客观不确定性指标的程序。(Sobol method used to calculate the variance of uncertainty based on objective indicators of the program.)