  A prior-guided deep learning (DL) based interference mitigation approach is
proposed for frequency modulated continuous wave (FMCW) radars. In this paper,
the interference mitigation problem is tackled as a regression problem.
Considering the complex-valued nature of radar signals, the complex-valued
convolutional neural network is utilized as an architecture for implementation,
which is different from the conventional real-valued counterparts. Meanwhile,
as the useful beat signals of FMCW radars and interferences exhibit different
distributions in the time-frequency domain, this prior feature is exploited as
a regularization term to avoid overfitting of the learned representation. The
effectiveness and accuracy of our proposed complex-valued fully convolutional
network (CV-FCN) based interference mitigation approach are verified and
analyzed through both simulated and measured radar signals. Compared to the
real-valued counterparts, the CV-FCN shows a better interference mitigation
performance with a potential of half memory reduction in low Signal to
Interference plus Noise Ratio (SINR) scenarios. Moreover, the CV-FCN trained
using only simulated data can be directly utilized for interference mitigation
in various measured radar signals and shows a superior generalization
capability. Furthermore, by incorporating the prior feature, the CV-FCN trained
on only 1/8 of the full data achieves comparable performance as that on the
full dataset in low SINR scenarios, and the training procedure converges
faster.
