Carrier-Free UWB Sensor Small-Sample Terrain Recognition Based on Improved ACGAN With Self-Attention

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.