Localization and Trajectory Estimation of Mobile Objects Using Minimum Samples

Identifying the spatial location of an object with reference to a known coordinate system is a critical localization problem in mobile sensors. In this paper, we present a novel method for the localization problem by only using a single sensor that knows its position, with the additional requirement that it is moving. The proposed method relies on multiple time samples by the moving sensor based on the received signal strength (RSS) and the angle of arrival (AOA). We also derive the Cramer-Rao bounds for the localization parameters. Based on the estimated location information over a brief time period, we further present robust trajectory-estimation techniques that employ Kalman filtering (KF). The performance of the proposed location and trajectory-estimation method is analyzed for different motion trajectories in a multihop sensor network. Based on computer simulations, we demonstrate that the proposed method reduces energy consumption by approximately 67% compared with traditional triangulation-based schemes.