TY - JOUR
T1 - Robust TDOA-Based Localization for IoT via Joint Source Position and NLOS Error Estimation
AU - Wang, Gang
AU - Zhu, Weichen
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received March 14, 2019; revised May 16, 2019; accepted May 20, 2019. Date of publication May 30, 2019; date of current version October 8, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61571249, in part by the Zhejiang Provincial Natural Science Foundation under Grant LY18F010011, and in part by the K. C. Wong Magna Fund in Ningbo University. (Corresponding author: Gang Wang.) G. Wang and W. Zhu are with the Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China (e-mail: wanggang@nbu.edu.cn; 975341907@qq.com).
Publisher Copyright:
© 2014 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Accurate localization is critical to facilitate location services for Internet of Things (IoT). It is particular challenging to provision localization based on nonline-of-sight (NLOS) signals. Thus, we actualize source localization based on time difference of arrival (TDOA) derived from NLOS signal propagations. The existing robust least squares (RLS) method exhibits two shortcomings: 1) it is formulated using too large upper bounds on the NLOS errors, and 2) it suffers from the possible inexact triangle inequality problem. Aiming at circumventing the shortcomings of the existing RLS method, we propose two new RLS formulations. On one hand, to reduce the upper bounds on the NLOS errors, we propose to jointly estimate the source position and the NLOS error in the reference path. On the other hand, to avoid using the triangle inequality, we introduce a 'balancing parameter' in the first formulation and develop the second formulation by transforming the measurement model. Both formulations are transformed via the S-lemma into optimization problems that are amendable to semidefinite relaxation. The proposed methods achieve superior performance over the existing methods, as validated by using both simulated and experimental data.
AB - Accurate localization is critical to facilitate location services for Internet of Things (IoT). It is particular challenging to provision localization based on nonline-of-sight (NLOS) signals. Thus, we actualize source localization based on time difference of arrival (TDOA) derived from NLOS signal propagations. The existing robust least squares (RLS) method exhibits two shortcomings: 1) it is formulated using too large upper bounds on the NLOS errors, and 2) it suffers from the possible inexact triangle inequality problem. Aiming at circumventing the shortcomings of the existing RLS method, we propose two new RLS formulations. On one hand, to reduce the upper bounds on the NLOS errors, we propose to jointly estimate the source position and the NLOS error in the reference path. On the other hand, to avoid using the triangle inequality, we introduce a 'balancing parameter' in the first formulation and develop the second formulation by transforming the measurement model. Both formulations are transformed via the S-lemma into optimization problems that are amendable to semidefinite relaxation. The proposed methods achieve superior performance over the existing methods, as validated by using both simulated and experimental data.
KW - Internet of Things (IoT)
KW - S-lemma
KW - nonline-of-sight (NLOS)
KW - semidefinite relaxation (SDR)
KW - time difference of arrival (TDOA) localization
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U2 - 10.1109/JIOT.2019.2920081
DO - 10.1109/JIOT.2019.2920081
M3 - Article
AN - SCOPUS:85073445505
SN - 2327-4662
VL - 6
SP - 8529
EP - 8541
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 5
M1 - 8726108
ER -