Indoor Trajectory Prediction Algorithm Based on Communication Analysis of Built-In Sensors in Mobile Terminals

In recent years, application services based on indoor locations have become increasingly popular, attracting extensive research attention. Among these application services, indoor location technology based on Wi-Fi signal fingerprints has developed rapidly. However, wireless signal transmission is easily affected by the environment, which will lead to deviations in the fingerprint location of the Wi-Fi signal. To improve the location accuracy and reduce the adverse effects of environmental factors, a method of fingerprint location for Wi-Fi signals assisted by smart phone built-in sensors is proposed, that is, to use the built-in sensors such as the accelerometer and gyroscope to collect data, calculate the user’s track information and track credibility, and combine the track information and signal fingerprint information to establish a comprehensive probability model, perform position matching, and determine the nearest reference point. The experimental results show that compared with the traditional fingerprint location method of Wi-Fi signals, the user trajectory information estimated from the sensor data can be used to effectively overcome the impact of environmental changes and improve the average location accuracy.