Ambiguity function (AF) modeling of radar signals is a powerful approach to feature extraction and recognition of radar emitters. An AF subspace based optimization framework is proposed to identify radar emitters by exploring unintentional modulation on pulse (UMOP) features. First, near-zero Doppler cuts of AF were extracted as a preliminary feature subset. Then, two kinds of cut-concatenation schemes were designed to construct two different pairs of feature vectors with complementary information respectively, which will facilitate the subsequent feature fusion via canonical correlation analysis (CCA) or discriminative canonical correlation analysis (DCCA). Theoretical analysis and experimental results show that the proposed algorithms not only alleviate the calculation problem in the existing AF based method, but also improve the recognition performance considerably, due to the successful information fusion and redundancy reduction conducted in the AF subset.