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.
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