Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters
This addresses interference problems in radar altimeters for aviation or autonomous systems, but is incremental as it builds on existing deep learning methods for signal processing.
The paper tackles interference mitigation in FMCW radar altimeters by proposing a Temporal Convolutional Network autoencoder that directly processes received signals, achieving superior interference suppression compared to a Least Mean Squares adaptive filter.
We investigate the end-to-end altitude estimation performance of a convolutional autoencoder-based interference mitigation approach for frequency-modulated continuous-wave (FMCW) radar altimeters. Specifically, we show that a Temporal Convolutional Network (TCN) autoencoder effectively exploits temporal correlations in the received signal, providing superior interference suppression compared to a Least Mean Squares (LMS) adaptive filter. Unlike existing approaches, the present method operates directly on the received FMCW signal. Additionally, we identify key challenges in applying deep learning to wideband FMCW interference mitigation and outline directions for future research to enhance real-time feasibility and generalization to arbitrary interference conditions.