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Learned Micro-Doppler Representations for Targets Classification Based on Spectrogram Images
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DOI：10.1109/ACCESS.2019.2943567.png)
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摘要
En 中文
This paper proposes a new approach for classifying ground moving targets captured by pulsed Doppler radar. Radar echo signals express the Doppler effect that moving targets produce. A learned feature representation extracted from spectrogram images using a transfer learning paradigm is proposed. A discrimination power analysis that derives highly discriminative features used to train a robust classifier was conducted. The extensive experiments performed on the public RadEch dataset show that the proposed method produces a significant boost in performance when compared to other state-of-the-art methods.
Keyword:
Target detection
micro-Doppler signatures
spectrograms
learned features representation
convolutional neural networks
transfer learning
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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