Radar target's HRRP always has some information redundancy, and is easily to be affected by noise or lack of separability. In this paper, using the advantage of kernel methods for solving nonlinear forms, we propose a radar target's HRRP feature extraction method based on Kernel Principal Component Analysis (KPCA) and a radar target fuzzy recognition method based on Support Vector Data Description (SVDD). In the course of feature extraction, KPCA method is used to reduce radar target's HRRP and to compress the dimension of HRRP, so that we can depress the noise and the sensitivity of target posture; in the course of recognition, we first find the smallest hyper-sphere including every class of training samples in feature space, then construct the fuzzy membership function according to the distance between every testing sample and the hyper-sphere surface, so we can recognize every testing sample based on its fuzzy membership. Simulation results of multi-target recognition reveal that the new method proposed in this paper not only achieves high recognition accuracy, but also has excellent generalization performance, for instance, we can achieve high recognition accuracy in lower SNR. So the new feature extraction and recognition method proposed in this paper is particularly suitable for radar target recognition.

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678805 (15 November 2007); doi: 10.1117/12.749426

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)