In recent years, research on Wi-Fi based indoor localization draws increasing attention. However, in practical applications, the localization error caused by device variance is a severe problem. In this paper, a new calibration-free and unsupervised method, SSDR (Signal Strength Difference Ratio) is proposed to solve this issue. Considering the signal variance between training devices and testing devices, SSDR first removes the linear effect of fingerprint to get new features. It then puts forward a distance calculation criterion with AP impact factor according to the effect of AP. Finally SSDR eliminates the variance of devices and realizes indoor localization based on the new features and distance calculation criterion. The experiment deployed in real indoor wireless environment shows, compared with traditional indoor localization methods, the proposed SSDR can increase the indoor localization accuracy by 10%~20%, which greatly improves the practical usability of indoor localization system.