An Improved Mobile Object Tracking Scheme Combining Range-Hybrid Localizations and Prediction Mechanisms

Localization is the most important feature in the sensor network environment, as it is a basic element enabling people and things to be aware of the surrounding environment. This paper proposes a mobile object tracking method, which aims to greatly improve mobile tracking accuracy in a low-density sensor network environment. The proposed scheme reduces the location estimation area using the information of surrounding nodes combining range-based and range-free techniques. It tries to conspicuously enhance the accuracy of mobile object tracking by adding an adaptive tracking prediction mechanism and estimative grid points, by considering the advance direction of the mobile object. Simulation results show that our method outperforms other localization and tracking methods in tracking accuracy.