Open AccessRIALS: RSU/INS‐aided localization system for GPS‐challenged road segments
Author(s) -
Zarza Himan,
Yousefi Saleh,
Benslimane Abderrahim
Publication year - 2016
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1002/wcm.2604
Subject(s) - ranging , computer science , inertial navigation system , global positioning system , beacon , real time computing , linearization , kalman filter , navigation system , electric beacon , overhead (engineering) , control theory (sociology) , inertial frame of reference , nonlinear system , telecommunications , artificial intelligence , physics , quantum mechanics , control (management) , operating system
This paper introduces a new vehicle localization approach for global positioning system‐challenged road segments (e.g., tunnels), which takes advantage of roadside units (RSUs) and in‐vehicle inertial navigation system (INS). In the proposed approach, namely RSU/INS‐aided localization system (RIALS), vehicles only need one RSU in their transmission range for an accurate positioning. The beacons received from the RSU along with the information provided by the INS system are used for establishing and maintaining particular locus circles. After linearization, the system of locus circle equations is solved using the linear least square estimation technique, and the estimated vehicle's position is obtained. In the presence of speed variations and existing ranging and INS estimation errors, the proposed RIALS adaptively sets the sufficient number of required locus circles, aiming at keeping the localization error below a given threshold. We study the effects of ranging and INS estimation errors on localization accuracy of RIALS from analytical and simulation perspectives. Results of extensive simulations show that the localization error is more sensitive to the ranging error than the INS error. Moreover, the network traffic overhead of the proposed method is considerably lower than other competitive localization approaches. Copyright © 2015 John Wiley & Sons, Ltd.