| Proj No. | A3176-251 |
| Title | Deep Learning Approach for Non-Line-of-Sight (NLOS) Positioning |
| Summary | Accurate indoor positioning is vital for applications like navigation, emergency response, and industrial automation. Non-line-of-sight (NLOS) conditions caused by obstacles such as walls, furniture, or humans significantly degrade the performance of traditional positioning systems. This project proposes a deep learning-based approach to improve positioning accuracy under NLOS conditions by identifying, compensating for, and mitigating NLOS-induced errors in real-time |
| Supervisor | A/P Tan Soon Yim (Loc:S1 > S1 B1B > S1 B1B 44, Ext: +65 67904505) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Communication Lab (Loc: S2-B4c-17) |
| Single/Group: | Single |
| Area: | Wireless and Communications Engineering |
| ISP/RI/SMP/SCP?: |