HRRP Target Recognition Based on the Dual-mode Gram Angle Field Features and the Multi-level CNN

• Published in: 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP) pp. 77 - 81
• Main Authors: Xu, Erchun, Xiong, Gang, Zhang, Shuning, Zhao, Huichang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.10.2021
• Subjects: CNN network; Convolutional neural networks; Correlation; Feature extraction; GAF features; HRRP target recognition; Logic gates; Radar; Support vector machines; Target recognition; UWB
• DOI: 10.1109/ICSIP52628.2021.9688620

Aiming at the Carrier-Free Ultra-Wide-Band Radar (CF-UWBR) under the near-range conditions, a novel target recognition method based on the Gram Angle Field (GAF) feature extraction and the Inception V3 network is proposed in this paper. The one-dimensional High-Resolution Range Profile (HRRP) of CF-UWBR is highly sensitive to observation angle so that the performance of direct recognition using the original time-domain HRRP data is not satisfying. In response to this problem, the GAF method is employed to convert the HRRP data into a two-dimensional image firstly, to maintain and highlight the correlation features between the adjacent time points in the polar coordinate domain. Furthermore, the Inception V3 network is selected for the target recognition based on the dual-mode GAF features, due to the excellent performance with the fewer computing resources. The experimental results on the actual measured CF-UWBR data set indicated that the proposed method is superior in performance to the methods based on the Support Vector Machine (SVM), the Gated Recurrent Unit (GRU) network, or the Convolutional Neural Network (CNN) with the original HRRP image. Especially when the dual-mode GAFs are used, the recognition accuracy of 97.63% can be achieved, about 1.95% higher than the STOA.