lab9_literki

这是一个用于线性调频雷达信号代码，可供参考(This is a code for linear FM radar signal, for reference)

说明：  运行环境matlab2006a，请下载！(Operating environment matlab2006a, please download!)

4从多通道acquisition.pdf多维信号重建滤波器组 (4Multidimensional filter bank signal reconstruction from multichannel acquisition.pdf )

用Matlab仿真的基于DRFM的等间隔直接转发干扰，内有时域和频域分析。(Matlab simulation with equal intervals based drfm forwarded directly interfere, sometimes within a domain and frequency domain analysis.)

分布式空时码的多符号差分检测，这是IEEE IEEE International Conference on Computing, Management and Telecommunications (ComManTel), Vietnam, April 2014的文章的对应仿真，是作者本人写的程序(Description simulation of the following paper: M. R. Avendi and Ha H. Nguyen, Multiple-Symbol Differential Detection for Distributed Space-Time Coding, IEEE International Conference on Computing, Management and Telecommunications (ComManTel), Vietnam, April 2014, Required Products MATLAB MATLAB release MATLAB 7.13 (R2011b) )

说明：  工序调整：因为实际因素的影响，在任务11开始之前必须先完成任务15，而在任务9（顶部施工）开始到第7周后必须穿插1周时间的安装工作，因而任务10中的“照明系统”工作周数也相应减少1周；(Adjustment process: since the actual factors, the task must be completed 11 missions before the start of 15, while in task 9 (top construction) began to be interspersed 7 weeks 1 week after the installation time, thus the task of 10 in &quot lighting systems &quot work week is also a corresponding reduction in the number of 1 week )

说明：  在原始信号加入白噪声，通过卡尔曼滤波去除噪声，恢复原始信号。(In the original signal to join the white noise through the kalman filter remove the noise, restore the original signal. )

经验模式分解，用于局域波分解，使用于多分量信号分析(empirical mode decomposition, for the local wave decomposition, in the use of multi-component signal analysis)

该代码可以用MATLAB实现序列图像的配准算法(The code can be realized using MATLAB image registration algorithm sequence)

说明：  数字图像修复算法的评价标准也是从两个方面进行评价的，一个是修 复算法的效率，一个是修复后图像质量。修复算法的效率主要就是看修复 图像所用的时间，可以用时间就行量化。而图像质量很难从量上唯一的进 行评价，目前图像修复质量还没有统一的评价标准，从实际应用出发，修 复后图像质量的好坏大多数是从以下两个方面就行评价，修复后图像给人 主观上的“满意度”和客观上图像的cnr、enl、epi、psnr、snr来评判。(The evaluation criteria of digital image restoration algorithms are also evaluated from two aspects, one is repair. One of the efficiency of complex algorithm is the image quality after restoration. The efficiency of the repair algorithm depends mainly on the repair. The time spent on the image can be quantified in terms of time. But image quality is difficult to improve quantitatively only. At present, there is no uniform evaluation criterion for image restoration quality. Starting from practical application, repair The quality of restored images is mostly evaluated from the following two aspects, and the restored images are given to people. Subjective "satisfaction" and objective image cnr, enl, epi, psnr, SNR to judge.)