Pedestrian Indoor Positioning Algorithms Based On Inertial Sensors And Map Matching

Location-based services have been rising for many years, with the continuous development of this service, the research on how to determine the location gradually goes deep. Outdoor location aware currently has a relatively mature system, it has been completely integrated into human life. In the field of indoor positioning, although there are a number of positioning system, but up to now, no system is unified or standard.This paper is based on the understanding of inertial sensor positioning system and Wi Fi indoor positioning system, and finally puts forward an indoor positioning solution using a smartphone contained inertial sensors and map matching algorithm. The core of inertial sensors based positioning is the pedestrian dead-reckoning algorithm. The algorithm is mainly formed by the step detecting, stride length calculating and heading estimating, after the initial position is given, current location is updated according to every step event composed of heading and stride length per a stride. We proposed a new stride detecting algorithm, which solves the problem of distinguishing between stand-still with arm action and walking. The detecting algorithm can monitor gait events, heading can be resolved by local rotation matrix measured in gyro and initial orientation calculated based on compass, stride length can be estimated through acceleration amplitude and time information, thus we can update current location every time there coming a new step from step gait event, which is called location tracking.However, the reliability of using a simple pedestrian dead-reckoning algorithm to get the position information is not high enough, so in this paper, we fused the information of map. Particle filter algorithm using boundary between accessible sections and inaccessible places to correct the weight of particle. As a result, all particles are generated with the sum of weights does not cross the line. Although it can solve the particles crossing border from a certain extent of particle filter, but when all particles are the over the boundary, it will lead the particle filter useless, so we use map matching algorithm. The walking and wireless network characteristics corresponds to some special geographical location, which when detected, current location can be corrected. In order to keep the walking path characteristics, the path matching algorithm matches the history path to the most similar one in the pre-defined paths, which is used for subsequent path tracking and screening. At the end of the paper, the Android application for smartphone is designed and realized, and the corresponding practical tests are carried out, which further reflects the advantages of the proposed algorithm.