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27 November 2019 Supervised kernel discriminant local tangent space alignment for high-resolution range profile-based radar target recognition
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Abstract

We present a modified local tangent space alignment (LTSA) algorithm, called supervised kernel discriminant local tangent space alignment (SKDLTSA), for radar target recognition based on high-resolution range profile (HRRP). SKDLTSA aims to extract intraclass geometric structure embedded in local neighborhoods, as well as to maximize interclass separability characterized by overall distances among different classes. It is formulated with kernel technique to extract nonlinear features, which helps to obtain better performance than its linear counterparts. Extensive experiments on measured HRRP data from three flying airplanes demonstrate that the proposed method can significantly improve the recognition performance. Further results also indicate its robustness to target attitude variations and noise corruption.

© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$28.00 © 2019 SPIE
Haohao Ren, Xuelian Yu, and Xuegang Wang "Supervised kernel discriminant local tangent space alignment for high-resolution range profile-based radar target recognition," Journal of Applied Remote Sensing 13(4), 046513 (27 November 2019). https://doi.org/10.1117/1.JRS.13.046513
Received: 8 May 2019; Accepted: 11 November 2019; Published: 27 November 2019
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