Real-Time Interference Mitigation for Reliable Target Detection with FMCW Radar in Interference Environments

Frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar systems are increasingly utilized in environmental sensing due to their high range resolution and robust sensing ability in severe weather environments. However, mutual interference among radar systems significantly degrades the target detection capability. Recent advancements in interference mitigation utilizing deep learning (DL) approaches have demonstrated promising results. DL-based approaches typically have high computational costs, which makes them unsuitable for real-time applications with strict latency requirements and limited computing resources. In this paper, we propose an efficient solution for real-time radar interference mitigation. A lightweight transformer, which is smaller and faster than the baseline transformer, is designed to reduce interference. The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. In addition, a two-stage knowledge distillation (KD) process is deployed to compress the network and enhance its efficiency. The staged distillation approach alleviates the training difficulties associated with substantial differences between the teacher and student networks. Both simulated and real-world experiments demonstrate that the proposed method outperforms the state-of-the-art methods while achieving high processing speeds.