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Generalized Chirplet-Squeezing Transform for Target Localization
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DOI：10.1109/TIM.2024.3451599.png)
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摘要
En 中文
In Doppler through-wall radar (TWR) target localization, accurately extracting the target's instantaneous frequency (IF) curve from the time-frequency analysis (TFA) result of the echo signal is crucial for precise target localization. However, the existing TFA techniques exhibit significant time-frequency aliasing and noise in the echo spectrogram when analyzing echo signals in multitarget scenarios, making it challenging to accurately estimate the target IF curve and achieve target localization. To solve this problem, this article proposes a postprocessing TFA method termed the generalized chirplet-squeezing transform (GCST), which employs a sequential enhancement strategy to effectively suppress time-frequency aliasing and noise by enhancing the time-frequency energy concentration for specific targets within the time-frequency distribution (TFD). Numerical simulations and moving target localization experiments indicate that, compared with the existing TFA-based localization methods, the proposed GCST method not only suppresses aliasing and noise in the TFD but also enhances the time-frequency energy concentration of the target. This ensures the accurate extraction of the IF curve and precise localization of moving targets.
Keyword:
Time-frequency analysis
Location awareness
Transforms
Noise
Demodulation
Accuracy
Receivers
Instantaneous frequency (IF)
through-wall radar (TWR)
time-frequency analysis (TFA)
time-frequency distribution (TFD)
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
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