Research And Application Of Multisensor Fusion Algorithm For Pedestrian Indoor Navigation And Localization

In different areas, it is of great significance to obtain the location of people or objects. The demand of location-based services(LBS) had reached a new level. Personal positioning has also made great progress.Positioning technology is divided into outdoor positioning and indoor positioning. Due to building blockage and other reasons, the traditional global positioning system (Global Positioning System, GPS) can not meet the needs of indoor use, therefore, different indoor positioning technology has been proposed.The smart phone inertial navigation components almost meet the need of indoor pedestrian positioning and navigation. In the indoor environment,the pedestrian movement and indoor maps in a specific location. For example, pedestrians will make turns at the corners of the room. So, in this paper, indoor location algorithm based on indoor map and mobile sensor is proposed.In this paper, we study the pedestrian movement characteristics, and makes the optimization and improvement of the traditional pedestrian track estimation. The motion characteristics of the pedestrian in the room are described and classified. The Hidden Markov Model (HMM) is established based on the indoor map and the motion characteristics of the pedestrian in the indoor specific position to correct the error of the inertial sensor by Viterbi algorithm.This paper presents a multi-sensor fusion algorithm based on pedestrian motion, indoor map. The localization system proposed in this paper is validated by experiments and simulations. Compared with the traditional indoor location method, the proposed algorithm has low dependence on the outside world, low consumption and high precision.