A new dual-input feature fusion network using RGB images and channel impulse responses identifies LoS/NLoS conditions for UAVs with up to 97.69% accuracy and reduces trilateration positioning error by about 70%.
Intelligent multi-modal sensing-communication inte- gration: Synesthesia of machines,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Sensing-Assisted LoS/NLoS Identification in Dynamic UAV Positioning Systems
A new dual-input feature fusion network using RGB images and channel impulse responses identifies LoS/NLoS conditions for UAVs with up to 97.69% accuracy and reduces trilateration positioning error by about 70%.