Hybrid TOA/AOA Localization Based on Semidefinite Programming in Non-Line-of-Sight Environment

摘要

With the rapid development of wireless technologies the demand for positioning services has grown dramatically over the past years Global Positioning System (GPS) is widely used in wireless devices for positioning However GPS receiver can not operate in the indoor because of the existence of Non-Line of Sight (NLOS) and the absence of available satellites In this work we investigate the hybrid localization technique based on Time of Arrival (TOA) and Angle of Arrival (AOA) information We assume that the estimator does not have any prior knowledge of path propagation type i e either LOS or NLOS Based on the range and angle measurements we derive the Maximum Likelihood (ML) estimator in this work which is a non convex optimization problem We show that the ML estimator can be transformed into a convex problem by applying the Semide nite Programming (SDP) relaxation technique which can be solved effciently To be robust in the NLOS environment the proposed SDP estimator treats each measured data with different importance by assigning a weight to each path Extensive simulations are performed to examine the performance of the proposed SDP estimator in numerous network con gurations A case study is also carried out to demonstrate the usefulness of the proposed SDP estimator in cellular networks

獎項日期	2017 8月 14
原文	English
監督員	Kuang-Hao Liu (Supervisor)