The multipath effect and movements of people in indoorenvironments lead to inaccurate localization. Through thetest, calculation and analysis on the received signal strength indication(RSSI) and the variance of RSSI, we propose a novelvariance-based fingerprint distance adjustment algorithm (VFDA).Based on the rule that variance decreases with the increase ofRSSI mean, VFDA calculates RSSI variance with the mean valueof received RSSIs. Then, we can get the correction weight. VFDAadjusts the fingerprint distances with the correction weight basedon the variance of RSSI, which is used to correct the fingerprintdistance. Besides, a threshold value is applied to VFDA to improveits performance further. VFDA and VFDA with the thresholdvalue are applied in two kinds of real typical indoor environmentsdeployed with several Wi-Fi access points. One is a quadrate labroom, and the other is a long and narrow corridor of a building.Experimental results and performance analysis show that in indoorenvironments, both VFDA and VFDA with the threshold havebetter positioning accuracy and environmental adaptability thanthe current typical positioning methods based on the k-nearestneighbor algorithm and the weighted k-nearest neighbor algorithmwith similar computational costs.