Indoor localization at 5GHz using Dynamic machine learning approach (DMLA)

In recent years, Wi-Fi based indoor localization using received signal strength (RSS) gets considerable attention. However, RSS based Wi-Fi localization at 2.4GHz is highly susceptible and unstable. We proposed dynamic machine learning approach (DMLA) at 5GHz to effectively localize Wi-Fi users by means of feed-forward neural network algorithm. The simulation result shows that 90% of tested result has less than 0.4m estimation error, and no result shows greater than 0.47m location estimation errors.