Invited Talk: Yifei Fan, Northwestern Polytechnical University

Special session 12: Artificial Intelligence in Radar Signal Processing

Short Bio: 
Yifei Fan received the B.S. degree in Information confrontation technology from Xidian University in 2011, and received the Ph.D. degree in signal and information processing from the National Laboratory of Radar Signal Processing (Xidian University, Xi’an, China) in 2016. Currently, he is an assistant researcher in School of Electronics and Information, Northwestern Polytechnical University (NWPU). He is the principal investigator of several projects, including the national natural science foundation of China (NSFC), natural science foundation of Shaanxi province. His research interests include radar signal processing , weak target detection and radar sea clutter analysis.

Title:  Weak Target detection based on deep neural network under sea clutter background

Abstract:
To upgrade the performance of the traditional radar target detecting method based on one certain threshold, this paper applies the deep learning network into target detection field, which regards radar target detection as a binary signal classification question. Since sea clutter exhibits non-stationary characteristics with high sea state condition, fractal properties of sea clutter are considered for target detection. In addition, fractal parameters of autoregressive (AR) spectrum are regarded as the feature inputs for deep learning network. Finally, real radar sea clutter data are applied for training the deep learning neutral network, and several datasets are selected to test the detecting performance of the network. From the binary classification results, the proposed method based on deep learning network performs a better detecting performance than traditional CFAR and fractal methods.