The invention discloses a
radiation source new individual identification method based on
signal enhancement double contrast learning, and belongs to the field of
software radio. According to the method, a multi-physical domain enhancement operator sequence is adopted, so that the risk that feature
distortion is possibly introduced in traditional data enhancement is overcome, and a high-quality feature discrimination basis is provided for subsequent comparative learning by constructing positive and
negative sample pairs with strong correlation. A feature decoupling architecture of double comparative learning is adopted, and the category discrimination
advantage of supervised comparative learning and the feature decoupling capability of unsupervised comparative learning are organically fused. Dynamic semi-
supervised clustering is realized by adopting a dual decision clustering mechanism, firstly, the number of potential categories is determined by utilizing density peak clustering DPC, secondly, a semi-supervised decision module is introduced to realize known category accurate identification and unknown category adaptive discovery of
radiation source individuals, and then new
radiation source individual identification is realized. The method is suitable for the field of
software radio, and high-precision radiation source new individual identification is realized.