RNN-based Interference Suppression Method for CS radar: Simulation and Experimental Evaluations

In recent years, high-resolution 77GHz onboard automotive radar has been extensively investigated for automated driving due to its high performance and low cost characteristics. As onboard CS (Chirp Sequence) radars' deployment density increases, inter-radar interference occurs which will increase target miss-detection and false-detection probabilities significantly. To address this critical and challenging problem, wideband interference suppression method using deep learning was proposed, in which the feasibility for performance improvement is validated based on simulations. In this study, we perform both simulation and experimental evaluations on RNN (recurrent neural network) based interference suppression method, in order to address the tradeoff between the model training time and interference suppression performance and validate its real-world applicability.