Augmenting Multidimensional Fingerprint With Wavelet Transform for Indoor Localization

Abstract

In indoor positioning systems, Wi-Fi fingerprint positioning often attracts the attention of researchers due to its advantages such as popularity and low cost. However, Wi-Fi fingerprint positioning relies on a large number of labeled fingerprints, and these fingerprints are prone to becoming invalid in dynamic environments, resulting in very frequent Wi-Fi fingerprint collection, which is unfavorable for the promotion of Wi-Fi fingerprint positioning. To address this issue, this work proposes a fingerprint augmentation method based on wavelet transform, which reduces the need for extensive fingerprint data collection while maintaining localization accuracy. Specifically, the method first utilizes the virtual positions of Wi-Fi access points (APs) to embed spatial information into the fingerprint sequence, transforming the 1-D fingerprint sequence into a multidimensional fingerprint grayscale image. Furthermore, various fingerprint augmentation schemes based on discrete wavelet transform (DWT) are proposed to expand the fingerprint database and further enhance the robustness of the positioning model. Finally, a series of schemes are designed to verify the effectiveness of the fingerprint augmentation schemes, and comparisons are made with similar works. The results indicate that the DWT-based fingerprint augmentation scheme proposed in this article effectively reduces the reliance on labeled fingerprints without a significant increase in time complexity. Compared to similar works, it demonstrates greater adaptability to dynamic environments.