The code about deep-learning based FMCW Radar interference mitigation.
Our paper:
[1] J. Wang, R. Li, Y. He and Y. Yang, "Prior-Guided Deep Interference Mitigation for FMCW Radars," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022, Art no. 5118316.
[2] R. Li, J. Wang, Y. He, Y. Yang and Y. Lang, "Deep Learning for Interference Mitigation in Time-Frequency Maps of FMCW Radars," 2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China, 2021, pp. 1883-1886.
To read the paper, see:
[1] https://ieeexplore.ieee.org/document/9908588
[2] https://ieeexplore.ieee.org/document/10028226
The matlab scripts in this fold are used to generate the beat signals acquired with FMCW radar system.
Details:
[1] "beatSig_FMCW.m" synthesizes the useful beat signals scattered from point targets.
[2] "beatSig_FMCW_mov.m" synthesizes the useful beat signals scattered from point moving targets.
[3] "beatInterfer_FMCW.m" generates the beat signals related to interference after the dechirping and low-pass filtering.
[4] "main.m" is a demon for the full signal generation, where the "beatSig_FMCW_mov" and "beatInterfer_FMCW" are used.
[5] "test.m" calculates the SINR and correlation coefficient of interfered signal and predicted signal.
[6] "test_plot_tf.m" is used to plot the signal's waveform, frequency spectrum, and t-f diagram, etc.
[7] "./realdata/realdata_make.m" generates the dataset of real-world radar interfered signals for testing.
[8] "./realdata/test_realdata.m" is similar to "test.m", but it is applicable to measured signals.
The python scripts in this fold are used for training and testing the interference mitigation models.
[1] "complexnn/*" includes a variety of the basic complex-valued modules.
[2] "train_256.py" is used to train the interference mitigation models.
[3] "test.py" is used for testing.
[4] "RV_FCN.py" builds the real-valued fully convolutional network.
[5] "CV_FCN.py" builds the complex-valued fully convolutional network.
%==========================================================
% Contact: Jianping Wang, [email protected]
% Contact: Runlong Li, [email protected]
%==========================================================