FUAS-Net: Feature-Oriented Unsupervised Network for FMCW Radar Interference Suppression

The widespread application of frequency-modulated continuous-wave (FMCW) radars leads to a significant increase in the risk of mutual interference. Conventional compressed sensing (CS)-based interference mitigation methods are limited by large computational capacity, troublesome parameter settings, and inefficient data processing. The supervised deep learning approach can overcome the above challenges; however, the measured data are hard to invert the labels. To address these problems, an effective framework, called the feature-oriented unsupervised adaptive suppression network (FUAS-Net), is proposed to suppress mutual interference between FMCW radars. FUAS-Net is mainly composed of an encoder and an interference restoration decoder. First, the signal model is constructed, and the task of interference suppression (IS) is modified into an optimization framework with regularizers. Then, the design of the encoder is inspired by approximate message passings (AMPs) by taking advantage of a priori information in the frequency domain to obtain targets. Second, the decoder is designed to complete the mapping from the target to the predicted interfered echo, alleviating the data dependency. FUAS-Net provides robust target extraction capabilities and optimizes parameters without ground truth. Finally, a novel training loss function is proposed that combines the mean square error (mse) and IS ratio (ISR) constraints to guarantee the power and sparsity of the targets. Both simulation and measured experiments validate the effectiveness of the proposed method, in comparison with existing state-of-the-art methods, and the results show that FUAS-Net can accomplish effective target extraction, expressing the potential for efficient IS.