Our paper on robustifying Wi-Fi localization by “Between-Location” data augmentation has been published in IEEE Sensors Journal.
In Wi-Fi fingerprint-based indoor localization, we need to acquire a dataset with a high-density dataset in the target environment in this framework.
To overcome the data acquisition cost problem, we propose a brand new data augmentation for Wi-Fi indoor localization named Between-Location (BL) data augmentation.
BL data augmentation generates the fingerprint data for the whole target environment with high density by only using the sparsely sampled data. This data augmentation drastically enables us to reduce data sampled locations while keeping the localization accuracy even if some target locations have no data.
From the experimental results, the localization with BL data augmentation using 10 % sampled location achieves the same accuracy with localization without data augmentation using all sampled locations.
Masato Sugasaki and Masamichi Shimosaka
Robustifying Wi-Fi localization by Between-Location data augmentation
IEEE Sensors Journal, (Early Access)