Omnidirectional motion classification with mono-static radar using micro-Doppler signatures

• Yang, Yang

  School of Electrical and Information Engineering, Tianjin University

• Hou, Chunping

  School of Electrical and Information Engineering, Tianjin University

• Lang, Yue

  School of Electrical and Information Engineering, Tianjin University

• Sakamoto, Takuya

  Graduate School of Engineering, Kyoto University

• He, Yuan

  School of Information and Communication Engineering, Beijing University of Posts and Telecommunications

• Xiang, Wei

  College of Science and Engineering, James Cook University

Abstract

In remote sensing, micro-Doppler signatures are widely used in moving target detection and automatic target recognition. However, since Doppler signatures are easily affected by the moving direction of the target, prior information of aspect angle is essential for spectral analysis. Thus, a micro-Doppler-based classifier is considered to be “angle-sensitive.” In this article, we propose an angle-insensitive classifier for the omnidirectional classification problem using the monostatic radar through a proposed new convolutional neural network. We further provide a sensible definition of “angle sensitivity, ” and perform experiments on two data sets obtained through simulations and measurements. The results demonstrate that the proposed algorithm outperforms both feature-based and existing deep-learning-based counterparts, and resolve the issue of angle sensitivity in micro-Doppler-based classification.

Journal

• IEEE Transactions on Geoscience and Remote Sensing

  IEEE Transactions on Geoscience and Remote Sensing 58 (5), 3574-3587, 2020-05

  Institute of Electrical and Electronics Engineers (IEEE)

Details

• CRID: 1050285299803104768
• NII Article ID: 120006865698
• ISSN: 01962892
• HANDLE: 2433/252366
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • CiNii Articles