DFF-EDR: An Indoor Fingerprint Location Technology Using Dynamic Fusion Features of Channel State Information and Improved Edit Distance on Real Sequence

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 information in fingerprint-based positioning systems because it can reflect the characteristics of the signal on multiple subcarriers. However, the random noise contained in the raw CSI information increases the likelihood 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 amplitude and phase data. Then, the improved edit distance on real sequence (IEDR) is used as a similarity metric for fingerprint matching. Based on the above studies, we propose a new indoor fingerprint positioning method, named DFF-EDR, for improving positioning performance. During the experimental stage, data were collected and analyzed in two typical indoor environments. The results show that the proposed localization 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 localization errors.

Automated summary

A new indoor fingerprint positioning method, called DFF-EDR, is proposed in this study to improve the feature resolution of the system by dynamically fusing features and using an improved edit distance metric. The method effectively reduces noise interference and positioning errors, showing promising performance in indoor localization.