Location Prediction for Tracking Moving Objects

Abstract

With the advances in mobile communication and position finding technology for tracking the positions of continuously moving objects, there comes a kind of novel applications (e.g., traffic control, meteorology monitoring, mobile computing, etc.) in which the locations of moving objects need to be maintained and processed. Existing methods for predicting the locations of moving objects assume that objects move according to linear functions. This severely limits their applicability, since in practice movement is more complex, and may follow drastically different motion patterns. In order to overcome the deficiency of existing methods, a novel location prediction model based on grey theory is proposed. In the proposed location prediction model, the GM is applied to predict the future location of uncertain moving objects. Comparing with linear prediction model, the proposed location prediction model not only relaxes the limitation to motion pattern of moving objects and the requirement to accuracy of sampling data, but also improves the predictive accuracy.