Abstract:

Radar high-resolution range profiles (HRRP) satisfy typical multimodal distribution. In radar HRRP target recognition and rejection, it is difficult to utilize a singe Gaussian kernel to describe the multimodal distribution. According to this, support vector data description (SVDD) was expanded from a single Gaussian kernel to a linear combination of multiple Gaussian kernels and then this combination is used to treat the recognition and rejection problem. Based on different degrees of freedom on the combinational coefficients, the resulting Multi-kernel SVDD could be expressed as different convex optimization problems: SOCP or SDP, and both of them could be solved with global optimal solutions. The proposed method employs more complicated kernel formations, and it can describe the multimodal distribution of HRRP data more flexibly in the high-dimensional feature space so as to improve the recognition and rejection performance. Experimental results show that the loss value of the new method is just 88.6％~93.2％ that of the single kernel SVDD.

Key words: high-resolution range profiles(HRRP), multimodal distribution, recognition, rejection, support vector data description(SVDD), multi-kernel SVDD, convex optimization