Improving accuracy of indoor localization system using ensemble learning

Recent innovations in Light-emitting diode (LED) technology and Internet of Things applications have promoted the development of visible light communication and localization applications. LED-based indoor positioning application has been a potential topic attracting the attention of many researchers because this positioning technique provides high accuracy, low cost, simple operation, and medium complexity. This paper focuses on analyzing the positioning quality with different LED layout structures. Furthermore, we consider the influence of noise in these models through the ensemble learning algorithm. We also combine the ensemble learning method with the trilateration algorithm in the proposed solution. The numerical simulation results show that the proposed solution respectively achieved a positioning accuracy of 0.023, 0.011, and 0.009 m when we considered the negative effect of all noises in 3 distinct layouts: 3 LEDs, 4LEDs, and 5 LEDs.