Radar HRRP Target Recognition Based on Blind-Denoising Deep Network

Feature representation based on the high resolution range profile(HRRP) is important in radar automatic target recognition(RATR). Traditional algorithms of feature extraction utilize hallow architectures and rarely address the challenges of high-noise and unknown-noise distribution. The capability of RATA is restricted by these challenges. In this paper, a novel blind-denoising network(BDNet) is proposed to implement denoising and automatically extract features. As an extension of deep autoencoder, BDNet is based on fully convolutional architecture and employs fusion layers to transfer input features to high dimensional space. Trained with noise-to-noise, BDNet can implement blind-denoising and doesn't rely on noise distribution. Then the output of BDNet is used to classify the targets. In the experiment, we use the measured HRRP signals of four aircrafts to show the effectiveness of our methods. The results prove that BDNet can achieve blind-denoising in high-noise environment and significantly improve the performance of recognition. And our proposed BDNet-AlexNet outperforms other recognition methods.