A Real-time Filtering Method of Positioning Data with Moving Window Mechanism

Abstract

Nowadays, advanced mobile devices can obtain current position with the help of positioning data systems such as GPS, GLONASS, Galileo, and so on. However, positioning data sets usually have erroneous data for various reasons, mainly due to the environmental issues as well as inherent systematical issues. While doing research related to positioning data sets, authors experienced quite a large number of erroneous positioning data using Apple iPhone or Samsung Galaxy devices, and thus need to filter evident errors. In this paper, we will suggest relatively simple, but efficient filtering method based on statistical approach. From the user’s mobile positioning data in a form of < latitude; longitude; time > obtained by mobile devices, we can calculate user’s speed and acceleration. From the idea of sliding window (moving window), we can calculate statistical parameters from speed and acceleration of user position data and thus filtering can be made with controllable parameters. We expect that the simplicity of our algorithm can be applied on portable mobile device with low computation power. For the possible enhancement of our method, we will focus on the construction of more precise window for better filtering. A backtracking interpolation was added in order to replace erroneous data with proper estimations in order to have more precise estimation of moving window. We also proposed this filtering algorithm with interpolation as a basis of future investigation in the section of conclusion and future research.

Keywords: Human Mobility, Error Filtering, Positioning Data, Moving Window, Sliding Window