Radio frequency fingerprinting (RFF) is a technique that identifies wireless devices by exploiting unique hardware-induced imperfections measured in their transmitted RF signals. We propose a lightweight RFF method by leveraging temporal variations in RF signals. By combining multi-scale feature extraction through coarse and fine segmentation with DTW-based similarity features, our approach achieves an accuracy of 0.9882 and a Macro-F1 score of 0.988 in our experiment using the Wi-Fi RF data collected from 120 devices.