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FmcwRadarSignalProcess-master

于 2021-01-07 发布
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下载积分: 1 下载次数: 4

代码说明:

说明:  FMCW信号处理是通过python脚本实现的,可以总结为: FMCW IF信号预处理 范围-时间地图构造(RTM) 多普勒时间地图构造(DTM) 角度时间地图构造(ATM)(FMCW signal process was implemented by python script, which can be sum up as: FMCW IF signal pre-process Range-Time-Map construction (RTM) Doppler-Time-Map construction (DTM) Angle-Time-Map construction (ATM))

文件列表:

FmcwRadarSignalProcess-master, 0 , 2019-05-05
FmcwRadarSignalProcess-master\README.MD, 1173 , 2019-05-05
FmcwRadarSignalProcess-master\__pycache__, 0 , 2019-05-05
FmcwRadarSignalProcess-master\__pycache__\pyradar.cpython-37.pyc, 2765 , 2019-05-05
FmcwRadarSignalProcess-master\__pycache__\tools.cpython-37.pyc, 4723 , 2019-05-05
FmcwRadarSignalProcess-master\copy_radar_system.py, 6406 , 2019-05-05
FmcwRadarSignalProcess-master\pyradar.py, 3146 , 2019-05-05
FmcwRadarSignalProcess-master\radar_system.py, 7071 , 2019-05-05
FmcwRadarSignalProcess-master\test.bin, 16777216 , 2019-05-05
FmcwRadarSignalProcess-master\tools.py, 9387 , 2019-05-05
FmcwRadarSignalProcess-master\ui_rda.py, 3683 , 2019-05-05
FmcwRadarSignalProcess-master\ui_rda.ui, 4124 , 2019-05-05

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  • 代码
    说明:  1、正弦函数 f (t) = K sin(wt +a) 2、矩形脉冲函数 f (t) = u(t) - u(t - t0 ) 3、抽样函数 4、单边指数函数 f (t) = Ke -t 5、已知信号 f1(t) = u(t + 2) - u(t - 2), f 2 (t) = cos(2pt) ,用 MATLAB 绘制 f1(t) + f 2 (t) 和 f1(t)′ f 2 (t)的波形。(such as : Sine function f (t) = K sin(wt +a) Rectangular pulse function f (t) = u(t)-u(t-t0) Sampling function One-sided exponential function f (t) = Ke -t Known signal f1(t) = u(t + 2)-u(t-2), f 2 (t) = cos(2pt), draw with MATLAB The waveforms of f1(t) + f 2 (t) and f1(t)*f 2 (t))
    2021-03-19 10:39:19下载
    积分:1
  • Desktop
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    积分:1
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    主要功能是实现相机标定,以及实现反投误差,实现特征点的提前。(The main function is to achieve camera calibration and error to achieve the anti-investment, to achieve the feature points in advance.)
    2014-01-07 15:06:33下载
    积分:1
  • toolbox-master
    说明:  该节主要介绍下matlab下的光流法以及一个研究物体检测方面的一个较好的工具箱 :Piotr’s Computer Vision Matlab Toolbox .将这两个工具箱及其里面所有文件的路径都添加到matlab路径中去,这样的话基本上是可以用了。 Piotr’s 的工具箱里面的函数很多,也涉及到了很多方面,更主要的是关于目标检测方面的。(This section mainly introduces the optical flow under Matlab and a better toolkit for researching object detection: Piotr 's Computer Vision Matlab Toolbox. Add the two toolkits and all the files in the path to the MATLAB path, which is basically useful. There are many functions in the toolbox of Piotr 's, and there are many aspects involved)
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