Singular Value Decomposition Based Indoor Localization Using Small Scale Crowd Sensing Data

Traditional crowd sensing based indoor localization methods rely on large scale pre-collected fingerprint data to construct a radio map with cumbersome prior preparation. However, when they lack floor plan information or only have a little of data is willing to share, the tracking accuracy degrades significantly. In this paper, we propose a singular value decomposition (SVD) track matching scheme to obtain an effective radio map based on small scale crowd sensing data, which is a non-learning based system (SVD-CSP). SVD-CSP fuses received signal strength indicator (RSSI), inertial measurement unit (IMU), and magnetic field strength to label surrounding WiFi access points as marker points. The proposed scheme uses SVD method to directly compute the rotation matrix and displacement vector among the crowd sensing trajectories and attain the reliable tracks. The radio map is constructed and users are tracked according to our developed bidirectional Bayesian filter, which contains forward filter and reverse filter. The density-based spatial clustering of applications with noise (DBSCAN) is embedded within the forward filter to improve the radio map quality. Meanwhile, the reverse filter fuses pedestrian dead reckoning (PDR) and radio map-based localization to track users. Experimental results demonstrate that SVD-CSP can achieve robust localization using extremely sparse crowd trajectories (e.g., 4 trajectories in a 648 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scenario, 30 trajectories in a 2856 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scenario) without deep learning training or infrastructure knowledge.