Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters

--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 T emporal 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. Precise and reliable altitude measurements are essential in a number of airborne applications. Radar altimeters are widely employed to obtain such measurements for objectives such as commercial aviation, the operation of unmanned aerial systems, and military missions at low altitudes. Due to the sensitive nature of these and related applications, it is crucial to understand all potential sources of system failure and performance degradation. A particularly notable threat to reliable radar altimeter operation is radio-frequency (RF) interference from neighboring systems, which could be either intentional or incidental. Commercial altimeters commonly operate in the 4. 2-4.4 GHz band, which has created concern among regulators given the increased proliferation of communication systems in the 1-6 GHz spectrum. For example, in January 2023, the Federal Aviation Administration (FAA) issued a deadline for all commercial airlines to upgrade altimeters to ensure safe operation in the vicinity of 5G C-Band signals. By September 2023, the entire US airline fleet had updated their altimeter equipment to ensure that the risk of 5G interference would be mitigated through 2027.