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

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 signa...

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Bibliographic Details
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
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24113288