Terrain Recognition Based on the Carrier-Free UWB Radar Using Stacked Denoising Autoencoder

The carrier-free ultra-wideband (UWB) sensor characterizes 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. In this paper, we propose a terrain recognition framework based on the carrier-free UWB sensor. For the purpose of extracting noise-robustness features, a deep network named stacked denoising autoencoder (SDAE) is developed. Given that the convolutional neural network (CNN) is insensitive to translation to some extent, we combine several CNNs as middle structures of the proposed model. 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.