TY - GEN
T1 - A novel TOA-based source localization algorithm in wireless sensor networks
AU - Feng, Renjian
AU - Li, Chenguang
AU - Ran, Qiu
AU - Wu, Yinfeng
AU - Yu, Ning
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/6
Y1 - 2018/8/6
N2 - With great development of wireless sensor networks (WSNs) technology, one of its important applications, source localization, has attracted numerous interests of recent scientific researches. Among different kinds of methods, techniques based on time difference of arrival (TDOA) and time of arrival (TOA) are practical and of high accuracy. In consideration of TDOA-based methods' inherent noise correlation and loss in signal-to-noise ratio (SNR), this paper focuses on TOA-based source localization method in WSNs. We formulate the localization problem as a maximum-likelihood (ML) problem, and exploit the relationship between the emitting time constant of signal and the coordinate of source to remove the time constant in the ML function. Then the conjugate gradient method is utilized to obtain the final solution. Simulation results validate the convergence of the proposed algorithm and demonstrate its better performance than some existing TDOA based techniques.
AB - With great development of wireless sensor networks (WSNs) technology, one of its important applications, source localization, has attracted numerous interests of recent scientific researches. Among different kinds of methods, techniques based on time difference of arrival (TDOA) and time of arrival (TOA) are practical and of high accuracy. In consideration of TDOA-based methods' inherent noise correlation and loss in signal-to-noise ratio (SNR), this paper focuses on TOA-based source localization method in WSNs. We formulate the localization problem as a maximum-likelihood (ML) problem, and exploit the relationship between the emitting time constant of signal and the coordinate of source to remove the time constant in the ML function. Then the conjugate gradient method is utilized to obtain the final solution. Simulation results validate the convergence of the proposed algorithm and demonstrate its better performance than some existing TDOA based techniques.
KW - maximum-likelihood
KW - source localization
KW - time of arrival
KW - wireless sensor networks
UR - https://www.scopus.com/pages/publications/85052377584
U2 - 10.1109/ICIST.2018.8426135
DO - 10.1109/ICIST.2018.8426135
M3 - 会议稿件
AN - SCOPUS:85052377584
SN - 9781538637814
T3 - 8th International Conference on Information Science and Technology, ICIST 2018
SP - 429
EP - 436
BT - 8th International Conference on Information Science and Technology, ICIST 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information Science and Technology, ICIST 2018
Y2 - 30 June 2018 through 6 July 2018
ER -