MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions

Scene-oriented multisource sensor fusion for smartphone pervasive indoor localization is the key to location-based services (LBS), which is of practical significance to addressing the limitations of indoor navigation satellite signals and facilitating accurate location services within the final 100 m. The rapid advancement of smartphone sensors and their performance provide a great opportunity for realizing smartphone indoor localization based on multisource sensors. However, the limited adaptability of current localization methods hinders their widespread applicability, necessitating the development of a smartphone-based indoor localization method tailored for complex indoor scenes. This article proposes a smartphone indoor localization method that integrates map location anchors (MLAs) with multisensor fusion location (MFL). The method identifies the sensor signal feature patterns of the smartphone’s built-in sensors in multiscenes and binds them to MLA to serve map matching. The MLA is also utilized to correct the cumulative error of pedestrian dead reckoning (PDR) to achieve indoor localization in multiple scenes by using fusion scheduling of different sensor modules. The experimental results show that the proposed method can achieve a localization accuracy of 1.01 m in multifloor scenes in collaboration with multisource sensor localization modules matched with MLA, with high robustness and usability. The code of this article is open source at
https://github.com/GHLJH/MLA-MFL
.