The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication(RSSI) and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm(VFDA).Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building.Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs. The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication (RSSI) and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm (VFDA VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that i n indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs.