Unintentional modulation (UIM), which is unavoidable and unique to individual emitters, can be used to reliably realize emitter identification. Previous identification methods extract features from either some parts of the signal, ignoring the UIM on the other parts, or immediately the whole signal, resulting in heavy computational loads. In this paper, we take the structure of UIM into consideration, and propose a new feature extraction scheme. We first analyze the mechanism of UIM, realizing that the jitter frequency or intensity of UIM is time-varying throughout the signal. The parts of the signal with slow jitters are then located, in which the bandwidths of UIM are actually much smaller than the initial sampling rate; thus, down-sampling is applied to these parts. Lastly, features extracted from different parts are combined. Experiments on real-world data validate the superior recognition performance and computing speed of the proposed feature extraction scheme.