In this repository, you can find python scripts for microwave imaging using a vector network analyzer (VNA) and a frequency-modulated continuous wave (FMCW) radar system.
The FMCW radar system is realized with commercially available components. More details can be found in my publications (https://ieeexplore.ieee.org/document/10305102) and (https://ieeexplore.ieee.org 7BFA /document/10590586).
As a VNA I used the E8361C from Keysight.
For the imaging with the vector network analyzer, the delay-and-sum (DAS) beamforming algorithm is implemented. For the FMCW radar system the standard matched filter approach is used. However, both approaches are analytically identical.
Much more details can be found in my journal paper, which is available as open access on IEEE Xplore (https://ieeexplore.ieee.org/document/10931040).
To compute an image based on a set of s-parameter measurements, it is necessary that you have a folder with the corresponding .s2p touchstone files. A separate touchstone file is required for every antenna position. The name of the file indicates the measurement position in mm, e.g. 0.0_0.0.s2p, 0.0_10.0.s2p, 0.0_20.0.s2p and so on. To generate the image, it is necessary to run the script Calculate_VNA_Image.py. In the variable 'path' you just have to specfiy the folder with your touchstone files. After the calculation of the image is completed, a subfolder is created in the "Pickle_Files" directory. The name of the subfolder is the date and time when the image was completed. The final image is saved in this folder as a pickle file. Additionally, there is a logfile in which all the used settings are stored.
For the image generation with the FMCW radar system you have to run the file Calculate_Radar_Image.py. The input data for Calculate_Radar_Image.py are .csv files that contain the output data of the radar system that was recorded using an oscilloscope. This includes the time values, the I-part of the IF signal and the Q-part of the IF signal. In the variable 'path' you just have to specfiy the folder with your .csv files. The name of the file indicates the measurement position in mm, e.g. 0.0_0.0.csv, 0.0_10.0.csv, 0.0_20.0.csv and so on.
After the calculation of the image is completed, a subfolder is created in the "Pickle_Files" directory. The name of the subfolder is the date and time when the image was completed. The final image is saved in this folder as a pickle file. Additionally, there is a logfile in which all the used settings are stored.
There is also the possibility to generate fancy animations of the radar image like the one above. To do this, you must first execute Calculate_Radar_Image_Animated.py and then Generate_GIF_File.py.
The script Calculate_Error_Function_Radar.py is used to calculate the error-function for the FMCW radar system. For ideal data the calibration is useless, since there are no errors that need to be corrected.
The repository contains two scripts (Ideal_VNA_Data_Generator.py and Ideal_Radar_Data_Generator.py) for the generation of ideal VNA and radar data. The ideal data is saved in the folders 'Ideal_Data_VNA' and 'Ideal_Data_Radar'. If these folders do not already exist they will be created automatically. The default settings are chosen so that you simply have to download the entire repository. Then you have to execute Ideal_VNA_Data_Generator.py and Imaging_VNA.py to get a VNA image. To generate a radar image, the correct order is Ideal_Radar_Data_Generator.py and Calculate_Radar_Image.py.
For the generation of the ideal data, the user has to specify an array of (x,y)-coordinates. To compute the ideal FMCW radar signals, the corresponding IF frequency and phase shift based on the ideal signal model is calculated. For the ideal VNA data, a set of microstrip lines with the corresponding lengths is simulated using the scikit-rf package.
In the folder Supporting_Material you can find some more information about the details of the signal processing.
If you have any comments feel free to write me an e-mail to m.maier@tu-braunschweig.de.
Exemplary measurement data is unfortunately too large for GitHub. If you are interested you can contact me and I will provide a Google Drive link where you can find measurement data.
I hope you find my scripts helpful!