Positioning technology based on wireless network signals in indoor environments has developed rapidly in recent years as the demand for location-based services continues to increase. Channel state information (CSI) can be used as location feature in-formation in fingerprint-based positioning systems be-cause it can refl ect the characteristics of the signal on multiple subcarriers. However, the random noise con-tained in the raw CSI information increases the like-lihood of confusion when matching fingerprint data. In this paper, the Dynamic Fusion Feature (DFF) is proposed as a new fingerprint formation method to remove the noise and improve the feature resolution of the system, which combines the pre-processed am-plitude and phase data. Then, the improved edit dis-tance on real sequence (IEDR) is used as a similarity metric for fingerprint matching. Based on the above studies, we propose a new indoor fingerprint position-ing method, named DFF-EDR, for improving posi-tioning performance. During the experimental stage, data were collected and analyzed in two typical indoor environments. The results show that the proposed lo-calization method in this paper effectively improves the feature resolution of the system in terms of both fingerprint features and similarity measures, has good anti-noise capability, and effectively reduces the local-ization errors.