An Adaptive Wi-Fi Trilateration-Based Indoor Localization

Global Positioning System (GPS) is widely used as the outdoor position system because of it provides the satisfactory results especially for outdoor navigation. However, it is not suitable for the circumstances of non-line of sight (NLoS) and complex indoor environments. To fill the gap, Wi-Fi signal has grasped the great attention being purposed as the solution for Indoor Positioning System (IPS). Existing enormous efforts have been exerted to focus on the Wi-Fi fingerprinting to achieve high accuracy. Through deep learning and machine learning, the current Wi-Fi fingerprinting can localize the object with the accuracy better than GPS. Nonetheless, it involves huge labor-cost calibration and high training complexity for intensive site survey and requires recalibration for a different environment. Due to these limitations, the current approach using Wi-Fi fingerprinting is impractical as the indoor localization solution to present-day scientific and enterprise interest. In this paper, we proposed an adaptive Wi-Fi trilateration-based indoor localization system which involves minimum labor-cost calibration to achieve a good accuracy, as a prominent research solution to location-based services (LBS).