DRVAT: Exploring RSSI series representation and attention model for indoor positioning

Haojun Ai*, Xu Sun, Jingjie Tao, Mengyun Liu, Shengchen Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Although Bluetooth Low Energy (BLE) fingerprinting localization has become a hot research topic with encouraging results, it is difficult to predict the location depending on a short duration received signal strength indication (RSSI) sequence in realistic scenarios due to the severe fluctuation of RSSI. We introduce a new perspective to view the indoor positioning problem by radio map fingerprint. We argue that even though beacons may be independently deployed, the RSSI series bear certain spatial relation because of their copresence in the same physical space. The latent relation implicitly conveyed by the coexistence of their signals at various indoor locations. Unlike existing approaches that try to find a direct mapping between sensed signals and the corresponding location, we explore the spatial relation of beacons from the input data to estimate location. We propose a deep learning localization system, termed DRVAT, which is based on the distributed representation vector (DRV) and self-attention (AT) among the pairs of MAC-RSSI. First, we obtain DRVs which represent dense features in low dimensionality through pre-training on all MAC-RSSIs. Then we exploit self-attention mechanism to learn the latent spatial relation of beacons. Finally, MAC-RSSIs labeled with locations are used to fine-tune the model for estimating location. Localization accuracy results demonstrated the superior performance as compared with other positioning methods, and the visualization of DRV and attention mechanism are consistent with the spatial deployment of BLE.

Original languageEnglish
Pages (from-to)4065-4091
Number of pages27
JournalInternational Journal of Intelligent Systems
Volume37
Issue number7
DOIs
Publication statusPublished - Jul 2022

Keywords

  • deep learning
  • fingerprints
  • indoor localization
  • radio map
  • self-attention

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