Indoor Trajectory Tracking Algorithm Based on Time Series Heuristic Information

Existing indoor trajectory tracking algorithms on wireless sensor network are based on continuous localization and can not make use of the heuristic information of RSSI time series within a certain temporal and spatial range. The heuristic information of RSSI time series is a key factor of trajectory tracking procedure. This paper proposes a new trajectory tracking algorithm on spatiotemporal correlation model based on heuristic information. According to the heuristic information related to moving trajectory, the new method contains the following essential phases. Firstly, we model the trajectory tracking model reflecting spatiotemporal correlation and statistical characteristics. Secondly, we detect spanning boundary event and judge which subarea the unknown node was in by means of information fusion of RSSI time series and moving least square method. Finally, the moving trajectory of unknown node is formed by means of dynamic time warping fingerprinting matching algorithm with heuristic information constraints. The principles of information fusion are strictly proved in mathematics. The field experiments and the simulation experiments show that the algorithm has good environment adaptability, smooth trajectory and the error does not accumulate among the subareas. Compared with the existing methods, the accuracy of trajectory tracked is improved.