Carrier-Free UWB Sensor Small-Sample Terrain Recognition Based on Improved ACGAN With Self-Attention
Carrier-Free UWB Sensor Small-Sample Terrain Recognition Based on Improved ACGAN With Self-Attention
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DOI：
10.1109/jsen.2022.3157894
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发表时间：
2022-04
影响因子：
4.3
通讯作者：
Xiaoxiong Li;Zelong Xiao;Yuying Zhu;Shuning Zhang;Si Chen
中科院分区：
文献类型：
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作者：
Xiaoxiong Li;Zelong Xiao;Yuying Zhu;Shuning Zhang;Si Chen
无载波超宽带传感器具有距离分辨率高、抗干扰能力强等特点。它不容易受天气和光照条件的影响，其接收的回波包含了目标的详细结构信息。提出了一种基于无载波超宽带传感器的小样本地形识别框架。利用地形回波信号的时频特征图进行分类。然而，样本不足使得分类器容易过拟合，因此本文提出了一种改进的辅助分类器生成对抗网络（IACGAN）用于数据增强。首先，在ACGAN的网络结构中加入了注意机制和多尺度卷积，提高了对回波信号时间特征图像的特征提取能力。其次，将训练器的真/假判断标准从Jensen-Shannon散度改为带梯度惩罚的Wasserstein距离，提高了训练的稳定性。最后，消除了分类器对生成样本的标签分类，从而进一步提高了生成图像的质量。实验表明，以IS和FID为生成质量评价标准，IACGAN提高了生成图像的质量。此外，k折交叉验证表明，IACGAN的数据增强提高了CNN分类器的识别率。最后，实验还发现，直接使用训练好的IACGAN中的SVM作为分类器，可以达到97%以上的准确率。这不需要在扩展的训练集上对分类器进行额外的训练，这是一种高效且低成本的替代方案。
The carrier-free UWB sensor features high distance resolution and high interference immunity. It is not easily affected by weather and lighting conditions, and its received echoes contain detailed structural information of the target. This paper proposes a small sample terrain recognition framework based on the carrier-free UWB sensor. The time-frequency feature maps of terrain echo signals are used for classification. However, insufficient samples make the classifier prone to overfitting, so we propose an Improved Auxiliary Classifier Generative Adversarial Network (IACGAN) for data enhancement in this paper. Firstly, attention mechanism and multi-scale convolution are added to the network structure of ACGAN to improve the feature extraction capability of time-feature images of echo signals. Secondly, the discriminator’s true/false judgment criterion changes from Jensen-Shannon divergence to Wasserstein distance with gradient penalty, improving training stability. Finally, label classification of the generated samples by the discriminator is eliminated, which further enhances the quality of the generated images. Experiments show that the IACGAN improves the quality of generated images with IS and FID as the generation quality evaluation criteria. Furthermore, k-fold cross-validation shows that data augmentation by IACGAN improves the recognition rate of the CNN classifier. Finally, the experiment also found that directly using the discriminator in the trained IACGAN as the classifier can achieve more than 97% accuracy. That does not require additional training of the classifier on the expanded training set, which is an efficient and low-cost alternative.