Radar high‐resolution range profile recognition via multi‐SV method. Issue 19 (31st July 2019)

• Record Type: Journal Article
• Authors: Li, Long; Jiang, Jie; Liu, Zheng
• Is Part Of: Journal of engineering. Volume 2019:Issue 19(2019)
• Page Start: 6327
• Page End: 6330
• Publication Date: 2019-07-31
• Subjects: image classification -- radar target recognition -- support vector machines -- feature extraction -- radar resolution; radar high‐resolution range profile recognition -- ground compound environment -- essential processes -- HRRP target recognition -- classification performance improvement -- novel target recognition method -- multiple support vector method -- training phase -- multiple correlate SV model -- multitarget feature space -- central‐coherence distributed regions -- multiple regions -- outlier discrimination -- targets classification -- practical radar target recognition task; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2019.0138
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : The radar high‐resolution range profile (HRRP) is of great significance for target recognition in the ground compound environment. As two essential processes of HRRP target recognition, discrimination and classification performance improvement are studied in this study. A novel target recognition method is designed, denoted by multiple support vector (SV) method. In the training phase, a multiple correlate SV model is constructed, which separates the multi‐target feature space into several central‐coherence distributed regions. Each region has been described by the modified SV method, and the category determination is based on the description of multiple regions. This method can overcome the issues of both outlier discrimination and targets classification. Due to the hierarchical process in the testing phase, this method needs less memory and calculation to fit the practical radar target recognition task. Finally, the experiment based on the measured data verifies the excellent performance of this method.