An Efficient Approach for Rigid Body Localization via a Single Base Station Using Direction of Arrive Measurement

• Published in: Ad Hoc Networks Vol. 306; pp. 220 - 230
• Main Authors: Wu, Shenglan, Ai, Lingyu, Zhan, Jichao, Yang, Le, Wu, Qiong, Zhou, Biao
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
• Subjects: Direction of arrival; Newton’s iteration algorithm; Rigid body localization; Single base station; The unit quaternion method
• ISBN: 3030372618; 9783030372613
• ISSN: 1867-8211; 1867-822X
• DOI: 10.1007/978-3-030-37262-0_18

Rigid bodies are objects whose profile will not change after moving or being forced. A framework of rigid body localization (RBL) is to estimate the position and the orientation of a rigid object. In a wireless node network (WSN) based RBL approach, a few wireless nodes are mounted on the surface of the rigid target. Even though the position of the rigid body is unknown, we know how the nodes are distributed, which means that the topology of the nodes is known. Recently, a novel RBL scheme is studied, in which the rigid target is localized with just one single base station (BS) by measuring the angles between the BS and the positions of wireless nodes in the current frame, i.e., direction of arrival (DOA). However, the DOA-based RBL model is highly nonlinear and existing heuristic algorithms are generally time-consuming. In this paper, we intend to find the optimal solution of the 3-D positions of wireless nodes by fusing the topology information and DOA measurements with Newton’s Iteration algorithm (NIA). Then, the rotation matrix and the translation vector can be obtained by the unit quaternion (UQ) method with the 3-D positions of wireless nodes, which completes the RBL task. Finally, we evaluate the proposed NIA-based RBL performance in terms of the root mean squared error (RMSE), as well as the computation costs.