TY - GEN
T1 - Robust Estimator for NLOS Error Mitigation in TOA-Based Localization
AU - Dong, Jing
AU - Luo, Xiaoqing
AU - Guan, Jian
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Localization based on range measurements may suffer from non-line-of-sight (NLOS) bias, which can significantly degrade the accuracy of localization. In this paper, the time-of-arrival (TOA) based localization problem in NLOS environments is addressed. In particular, we approximately model the hybrid noise formed by measurement noise and NLOS bias errors with a Gaussian distribution, and develop a robust estimator based on maximum likelihood (ML) which can mitigate the NLOS bias errors while estimating the location of the source. The Lagrange programming neural network (LPNN) is then applied to address the obtained nonlinear constrained optimization problem. Furthermore, a weighted version of the proposed algorithm is developed by incorporating the distances as weight factors in the formulation. Simulation results show that the proposed algorithms can provide better results as compared with several the state-of-the-art methods.
AB - Localization based on range measurements may suffer from non-line-of-sight (NLOS) bias, which can significantly degrade the accuracy of localization. In this paper, the time-of-arrival (TOA) based localization problem in NLOS environments is addressed. In particular, we approximately model the hybrid noise formed by measurement noise and NLOS bias errors with a Gaussian distribution, and develop a robust estimator based on maximum likelihood (ML) which can mitigate the NLOS bias errors while estimating the location of the source. The Lagrange programming neural network (LPNN) is then applied to address the obtained nonlinear constrained optimization problem. Furthermore, a weighted version of the proposed algorithm is developed by incorporating the distances as weight factors in the formulation. Simulation results show that the proposed algorithms can provide better results as compared with several the state-of-the-art methods.
KW - Lagrange programming neural network (LPNN)
KW - Maximum likelihood (ML) estimation
KW - Non-line-of-sight (NLOS)
KW - Time-of-arrival (TOA)
UR - https://www.scopus.com/pages/publications/85115726775
U2 - 10.1007/978-3-030-86137-7_7
DO - 10.1007/978-3-030-86137-7_7
M3 - 会议稿件
AN - SCOPUS:85115726775
SN - 9783030861360
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 67
BT - Wireless Algorithms, Systems, and Applications - 16th International Conference, WASA 2021, Proceedings
A2 - Liu, Zhe
A2 - Wu, Fan
A2 - Das, Sajal K.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2021
Y2 - 25 June 2021 through 27 June 2021
ER -