RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation

Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. In low-altitude environments, where unmanned aerial vehicle (UAV)-borne FMCW radars are increasingly used, mutual interference poses significant challenges to radar performance. In this article, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed using an end-to-end transformer-based structure. In the RIMformer, a dual multihead self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. In addition, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. Extensive simulations and empirical measurements demonstrate that RIMformer significantly improves interference mitigation and signal recovery, which advances the reliability and effectiveness of UAV-borne FMCW radars in complex environments.