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| Citation: | WAN Xianrong, XIE Deqiang, YI Jianxin, et al. Micro-Doppler clutter removal method based on the cancelation of sliding STFT spectrogram[J]. Journal of Radars, 2022, 11(5): 794–804. doi: 10.12000/JR22157
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Micro-Doppler Clutter Removal Method Based on the Cancelation of Sliding STFT Spectrogram
DOI: 10.12000/JR22157
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Abstract
Micro-motion clutter typically exhibits significant Doppler broadening, raises the noise floor, and annihilates weak targets, resulting in false alarms and missed detections. Removing micro-motion clutter effectively is critical to improving radar performance. In this study, a micro-motion clutter removal method based on the cancelation of the Short-Time Fourier Transform (STFT) spectrogram is proposed using the difference in the morphological performance of the constant-speed target echo and micro-motion clutter in the STFT spectrogram. The target echo appears in the STFT spectrogram as a linear energy strip parallel to the time axis on a specific frequency unit, whereas the micro-motion clutter appears as time-varying complex shapes across many frequency units due to its time-varying non-stationary characteristics. When the original STFT spectrogram slides along the time dimension to obtain the new STFT spectrograms, the target echo is distributed in the same position, whereas the position of the micro-motion clutter is different. Therefore, subtracting the above spectrograms, the target echo and the micro-motion clutter can be separated based on the intensity changes in each unit of the STFT spectrogram before and after subtraction, and the micro-motion clutter can be removed. The simulation and field experimental results validate the proposed method’s effectiveness. Compared with the common time-frequency-transform-based L-statistics algorithm, the proposed method can remove micro-motion clutter while retaining the target echo. -
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References
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Figure 1. Validation of the COLA compliant for Hamming window when
$R = {N_w}/2$ - Figure 2. Diagram of the basic principle of the proposed method
- Figure 3. Diagram of the sliding window
- Figure 4. Diagram of the STFT-SSC method
- Figure 5. Spectrum and STFT spectrogram of the simulated signal with multiple scattering points micro-motion clutter
- Figure 6. Comparison of the processing results between the STFT-SSC and L-statistics for multi-scattering points micro-motion clutter
- Figure 7. Spectrum and STFT spectrogram of the rotating blade micro-motion clutter simulation signal
- Figure 8. Comparison of the processing results between the STFT-SSC and L-statistics for rotating blade micro-motion clutter
- Figure 9. Experimental scene
- Figure 10. Spectrum and STFT spectrogram of the original signal
- Figure 11. Comparison of the processing results of field experimental data between the STFT-SSC and L-statistics