원문정보
초록
영어
Bluetooth Low Energy (BLE) based indoor positioning systems rely on accurately classifying channel conditions such as line-of-sight (LOS) or non-line-of-sight (NLOS). However, classification models trained in one building rarely generalize to another due to different floor layouts, anchor deployment, and interference patterns. The existing solutions often assume rich channel features, require labels from each new environment, or depend on fixed anchor layouts, which limit their scalability. We propose a BLE based domain adaptive RF channel classification that incorporates adversarial domain alignment and confidence-based pseudo-labeling to leverage unlabeled target data. We evaluate the approach using BLE Received Signal Strength Indicator (RSSI) data collected from three indoor areas: a corridor (source domain), a classroom (target domain), and an office room (unseen test domain). The proposed approach shows 2% gain over the no adaptive classification framework.
목차
I. INTRODUCTION
II. RELATED WORK
III. METHODOLOGY
A. Data Preprocessing and Sliding Windows Segmenttaion
B. Tokenization and Padding
C. Domain Adverseral Neural Network
D. Losses and Adversarial Allignment
IV. DATASET SETUP
V. EXPERIMENTAL RESULTS
VI. CONCLUSION
ACKNOWLEDGMENT
REFERENCES