Efficient AoA-Based Rigid Body Localization via Single Base Station for Internet of Things Applications

For many data mining applications under the Internet of Things (IoT) environments, the attitude and the position of a rigid target are indispensable and hidden information to be dug. The term Rigid Body Localization (RBL) refers to simultaneously estimation of the position and the attitude of a rigid target. The RBL framework which adopts only one single base station (BS) is considered in this paper for IoT applications. Several wireless sensor nodes with known topology information are fixed on the surface of the rigid target. The single BS fuses the angle of arrival (AoA) measurements from the nodes with the topology information for the RBL purpose. In this paper, we propose a two-stage RBL method to efficiently fusing the aforementioned two pieces of information. Firstly, we built the maximum likelihood estimator (MLE) of the information fusion and adopted the modified Newton's iteration algorithm (mNIA) to determine the wireless node position; then we used the unit quaternion (UQ) algorithm for estimating the relative position and attitude with respect to the predetermined reference state, which completed the RBL task. Finally, we evaluated the proposed RBL performance in terms of the root mean squared error (RMSE), convergence success rate, as well as the computation costs. Simulation results showe that the proposed mNIA-based RBL algorithm can achieve a finer RBL performance with obviously higher speed and 100 percent success convergence rate, comparing with existing heuristic methods.