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CNN-Based Time-Frequency Image Enhancement Algorithm for Target Tracking Using Doppler Through-Wall Radar
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DOI：10.1109/LGRS.2023.3282700.png)
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In target tracking applications by Doppler through-wall radar (TWR), the frequency ambiguity issue is a notable drawback, which can severely degrade the target localization performance. To overcome this drawback, a convolutional neural networks (CNNs) based target tracking algorithm is proposed in this letter. First, the short-time Fourier transform (STFT) is used to acquire an echo spectrogram. Then, the spectrogram resolution is enhanced by introducing a modified CNN module. Compared with the traditional CNN module, an additional convolutional weight block with a residual structure is designed to help extract deeper time-frequency features. Finally, the target instantaneous frequency (IF) curves are extracted from the enhanced spectrogram and the target positions are obtained. Experimental results prove that the proposed method can effectively enhance the spectrogram resolution and suppress the frequency ambiguity issue, which would lead to higher target localization accuracy and better robustness than traditional target tracking approaches.
Keyword:
Convolutional neural network (CNN)
Doppler through-wall radar (TWR)
frequency ambiguity issue
instantaneous frequency (IF)
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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