Specific emitter identification (SEI) is gaining popularity since it can distinguish different individuals in same type of radar emitter under complex electromagnetic environment. However, classification of signals is still a challenging task when the feature has low physical representation. In this work, we propose a compressed sensing mask feature in ambiguity domain, which can significantly improve the recognition rate of civil flight radar emitters. Furthermore, it not only represents physical characteristics of measured radar signals but also contains more time varying information and alleviates the computational costs. The physical significance and effectiveness of the proposed feature can be verified by reconstructing Wigner-Ville distribution (WVD) from the sparsest ambiguity function. Experimental results corroborate the highly accuracy and stability of the proposed approach.