Abstract
In this letter, we propose a dropout autoencoder fingerprint augmentation approach for enhanced Wi-Fi fine time measurement and received signal strength signals-based indoor localization. Due to complex indoor environment, fingerprinting techniques suffers from unrecorded measurements at some reference points, leading to incomplete fingerprint datasets. The dropout autoencoder was employed to reconstruct clean signal features for the unrecorded fingerprint measurement which can significantly affect the localization accuracy of fingerprinting systems. The localization is accomplished by utilizing deep neural networks (DNN)-based regression. We collected two datasets from experiments conducted in two indoor offices using commercial off-the-shelf devices. The performance of our proposed method was compared to existing methods, and on the respective datasets, our proposal method showed better performance with a localization accuracy of 0.3 m and 0.6m for the 1- percentile errors and 0.66m and 1.5m for the 2- percentile errors.
| Original language | English |
|---|---|
| Pages (from-to) | 1759-1763 |
| Number of pages | 5 |
| Journal | IEEE Communications Letters |
| Volume | 27 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1 Jul 2023 |
| MoE publication type | A1 Journal article-refereed |
Funding
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2023R1A2C3002890). This work was also done jointly in the LuxTurrim5G+ project funded by Business Finland and the KIOS CoE supported by the European Union's Horizon 2020 research and innovation Programme under grant agreement No 739551 and from the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy and the Huawei Technologies research grant No. HO2019085001.
Keywords
- DNN
- dropout autoencoders
- FTM-RSS
- Indoor localization
- missing fingerprints