An Interference Mitigation Method for FMCW Radar Based on Time-Frequency Distribution and Dual-Domain Fusion Filtering

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-05, Vol.24 (11), p.3288
• Main Authors: Zhou, Yu, Cao, Ronggang, Zhang, Anqi, Li, Ping
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; convolutional neural network; Deep learning; dual-tree complex wavelet transform; Equipment and supplies; False alarms; Fourier transforms; Frequency distribution; Frequency modulation; Image processing; interference mitigation; Methods; Neural networks; Optimization; Parameter estimation; Radar systems; Signal processing; Sparsity; synchrosqueezed transform; time–frequency transform
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24113288

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time-frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar's beat signal transformation from time domain to time-frequency domain, thus converting the interference mitigation to the task of time-frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time-frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.