One-class classifier for HFGWR ship detection using similarity-dissimilarity representation

Ship detection in high frequency ground wave radar can be approached by one-class classifier where ship echoes are regarded as abnormal situations to typical ocean clutter. In this paper we consider the problems of feature extraction and representation problems. We first study characters of ocean clutter and ship echo, and find that initial frequency and chirp rate are two proper features to tell difference between ship echoes and ocean clutters. However to lower the probability of misjudging, we represent data examples in a combined similarity-dissimilarity space other than using these two features directly. A hypersphere with minimal volume is adopted to bound training examples, from which an efficient one-class classifier is established upon limited number of typical examples. The comparison result to a one-class classifier based on original feature representation is given.