Ground Target Recognition Using Carrier-Free UWB Radar Sensor With a Semi-Supervised Stacked Convolutional Denoising Autoencoder

This paper proposes a scheme for ground-target recognition based on the carrier-free ultra-wideband (UWB) radar sensor for the first time. Carrier-free UWB system emits an extremely short pulse, which can provide potential advantages over other UWB systems, such as high range resolution, short blind...

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Veröffentlicht in:IEEE sensors journal Jg. 21; H. 18; S. 20685 - 20693
Hauptverfasser: Zhu, Yuying, Zhang, Shuning, Li, Xiaoxiong, Zhao, Huichang, Zhu, Lingzhi, Chen, Si
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 15.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
1D-convolutional neural networks
Algorithms
carrier-free UWB radar
Classification
Complex target recognition
Decoding
Deep learning
Echoes
Encoding
Feature extraction
Jamming
multi-level label
Noise reduction
Regularization
semi-supervised SCDAE
Short pulses
stacked denoising autoencoders
Target recognition
Ultra wideband radar
Ultrawideband radar
Unsupervised learning
ISSN:1530-437X, 1558-1748
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Zusammenfassung:This paper proposes a scheme for ground-target recognition based on the carrier-free ultra-wideband (UWB) radar sensor for the first time. Carrier-free UWB system emits an extremely short pulse, which can provide potential advantages over other UWB systems, such as high range resolution, short blind range, enhanced anti-multipath jamming ability. These characteristics above guarantee that UWB echoes contain more comprehensive and detailed information with respect to the target of interest. Feature extraction is fundamental and crucial for target recognition. In this paper a deep network named semi-supervised stacked convolutional denoising autoencoder (SCDAE) is developed to extract discriminative features. As an extension of stacked denoising autoencoders (SDAE), SCDAE replaces fully-connected layers with one-dimensional convolutional layers as middle structures. In order to capture essential signatures exactly and improve classification accuracy, we build up a semi-supervised learning mechanism via binding a label regularization term with SCDAE. Moreover, given that echoes observed at different angles belonging to the same target are different, a new multi-level label coding method is proposed and embedded in SCDAE. Experimental results demonstrate that the proposed algorithm can effectively learn essential representation and improve classification accuracy in the presence of low signal-to-noise ratios (SNRs), making it very suitable for use in a classification scheme.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3099823