Multiclass multiple kernel learning for HRRP-based radar target recognition

Yu Guo; Huaitie Xiao; Hongqi Fan; Yongfeng Zhu

doi:10.1117/12.2280252

19 June 2017 Multiclass multiple kernel learning for HRRP-based radar target recognition

Yu Guo, Huaitie Xiao, Hongqi Fan, Yongfeng Zhu

Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044306 (2017) https://doi.org/10.1117/12.2280252
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore

Abstract

A novel machine learning method named multiclass multiple kernel learning based on support vector data description with negative (MMKL-NSVDD) 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 proposed method not only inherits the close nonlinear boundary advantage of SVDD-neg model, which is applied with no assumptions regarding to the distribution of data and prior information, but also incorporates multiple kernel into the mode, avoiding fussy choice of kernel parameters and fusing multiple kernel information. Hence, it leads to a remarkable improvement of recognition rate, demonstrated by experimental results based on HRRPs of four aircrafts. The MMKL-NSVDD is ideal for HRRPBased radar target recognition.

Citation Download Citation

Yu Guo, Huaitie Xiao, Hongqi Fan, and Yongfeng Zhu "Multiclass multiple kernel learning for HRRP-based radar target recognition", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044306 (19 June 2017); https://doi.org/10.1117/12.2280252

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