SAR multi-target interactive motion recognition based on convolutional neural networks

• Published in: IET image processing 2020-09, Vol.14 (11), p.2567-2578
• Main Authors: Huan, Ruo-Hong, Ge, Luo-Qi, Yang, Peng, Xie, Chao-Jie, Chi, Kai-Kai, Mao, Ke-Ji, Pan, Yun
• Format: Article
• Language: English
• Subjects: feature extraction; image denoising; interactive motion feature extraction; multitarget interactive motion type recognition method; multitarget motions; neural nets; object detection; object recognition; radar imaging; radar target recognition; Research Article; SAR multitarget interactive motion recognition; SAR target images; signal denoising; synthetic aperture radar; synthetic aperture radar multitarget; target recognition; target tracking; target type recognition; wavelet transforms
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2019.0861

Synthetic aperture radar (SAR) multi-target interactive motion recognition classifies the type of interactive motion and generates descriptions of the interactive motions at the semantic level by considering the relevance of multi-target motions. A method for SAR multi-target interactive motion recognition is proposed, which includes moving target detection, target type recognition, interactive motion feature extraction, and multi-target interactive motion type recognition. Wavelet thresholding denoising combined with a convolutional neural network (CNN) is proposed for target type recognition. The method performs wavelet thresholding denoising on SAR target images and then uses an eight-layer CNN named EilNet to achieve target recognition. After target type recognition, a multi-target interactive motion type recognition method is proposed. A motion feature matrix is constructed for recognition and a four-layer CNN named FolNet is designed to perform interactive motion type recognition. A motion simulation dataset based on the MSTAR dataset is built, which includes four kinds of interactive motions by two moving targets. The experimental results show that the recognition performance of the authors’ Wavelet + EilNet method for target type recognition and FolNet for multi-target interactive motion type recognition are both better than other methods. Thus, the proposed method is an effective method for SAR multi-target interactive motion recognition.