Least square support vector data description for HRRP-based radar target recognition

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• 作者：Yu Guo ; Huaitie Xiao ; Qiang Fu
• 关键词：Machine learning ; Least square ; Support vector data description ; High ; resolution range profile ; Radar automatic target recognition
• 刊名：Applied Intelligence
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：46
• 期：2
• 页码：365-372
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
• 出版者：Springer US
• ISSN：1573-7497
• 卷排序：46

文摘

A novel machine learning method named least square support vector data description (LSSVDD) is developed to classify the FFT-magnitude feature of complex high-resolution range profile (HRRP), motivated by the problem of radar automatic target recognition (RATR). The LSSVDD method not only inherits the advantage of LSSVM model, which owns low computational complexity with linear equality constraints, but also overcomes the shortcoming of poor capacity of variable targets in SVDD. Similar to the LSSVM, the distribution information within classes is found by least square method and applied for adjusting the boundary in LSSVDD, which relieves the over-fitting of SVDD. Hence, there will be a remarkable improvement in classification accuracy and generalization performance. Numerical experiments based on several publicly UCI datasets and HRRPs of four aircrafts are taken to compare the proposed method with other available approaches, and the results especially for multiple targets can demonstrate the feasibility and superiority of the proposed method. The LSSVDD is ideal for HRRP-Based radar target recognition.