Research and Implementation of Key Techniques for Indoor Movement Object Trajectory Prediction

Abstract

With the popularity of smart mobile terminals and Wi-Fi signals, people’s demand for indoor location services has also increased. However, in the indoor space, GPS positioning is inaccurate, and the Wi-Fi signal may also have signal instability even no signal in some areas. This paper proposes a prediction method based on improved HMM model combined with historical trajectory clustering. The experimental results on UJIIndoorLoc data set show that the predicting trajectories can greatly improve the real-time performance of location services and the proposed method has great improvement in accuracy and scalability comparing with another model.