HRRP-based target recognition with deep contractive neural network.

Yilu Ma; Li Zhu; Yuehua Li

One of the radar high resolution range profile (HRRP) target recognition issues is the existence of noise interference, especially for the ground target. The recognition performance of traditional shallow methods degrades as suffering from the limited capability of extracting robust and discriminative features. In this paper, a novel deep neural network called stacked denoising and contractive autoencoder (SDCAE) is designed for millimeter wave radar HRRP recognition. To enhance the capability of learning robust structure and correlations from corrupted HRRP data, a denoising contractive auto-encoder is designed by combining the advantages of denoising auto-encoder and contractive auto-encoder. As an extension of deep auto-encoders, SDCAE inherits the advantage of enhancing the robustness of features via reducing external noise, retaining local invariance to obtain more discriminative representations of training samples. Experimental results demonstrate the superior performance of the proposed method over traditional methods, especially in noise interference condition and with few training samples.

ARTIFICIAL neural networks; MILLIMETER waves

Journal of Electromagnetic Waves & Applications, 2019, Vol 33, Issue 7, p911

0920-5071

Academic Journal

10.1080/09205071.2018.1540309