TY - GEN
T1 - Enhanced Vehicular Localization under Non-Line-of-Sight Multipath Propagation
AU - Choi, Sung Il
AU - Kim, Hong Ki
AU - Lee, Sang Hyun
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT). (No. 2021-0-00467, Intelligent 6G Wireless Access System, and No. 2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services.)
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - This paper develops localization strategies of non-line-of-sight (NLoS) hidden vehicles using Bayesian inference techniques and study an enhanced convergence property via statistical physics based analysis methods. NLoS vehicular localization is achieved with the measurement of multi-path channel propagation information, such as angle-of-arrival (AoA), angle-of-departure (AoD) and time-of-arrival (ToA) by characterizing their geometric relationship in a probabilistic inference task. An efficient solution is derived using generalized approximate message passing (GAMP), while convergence dynamics of GAMP has been reported as a critical issue with this approach. This paper develops a novel approach called generalized message passing (GMP) that revolves the convergence problem via compensation based on Onsager terms of the interaction model. Numerical results verify that the developed algorithm stabilizes the localization performance.
AB - This paper develops localization strategies of non-line-of-sight (NLoS) hidden vehicles using Bayesian inference techniques and study an enhanced convergence property via statistical physics based analysis methods. NLoS vehicular localization is achieved with the measurement of multi-path channel propagation information, such as angle-of-arrival (AoA), angle-of-departure (AoD) and time-of-arrival (ToA) by characterizing their geometric relationship in a probabilistic inference task. An efficient solution is derived using generalized approximate message passing (GAMP), while convergence dynamics of GAMP has been reported as a critical issue with this approach. This paper develops a novel approach called generalized message passing (GMP) that revolves the convergence problem via compensation based on Onsager terms of the interaction model. Numerical results verify that the developed algorithm stabilizes the localization performance.
UR - http://www.scopus.com/inward/record.url?scp=85122919427&partnerID=8YFLogxK
U2 - 10.1109/ICTC52510.2021.9620752
DO - 10.1109/ICTC52510.2021.9620752
M3 - Conference contribution
AN - SCOPUS:85122919427
T3 - International Conference on ICT Convergence
SP - 318
EP - 322
BT - ICTC 2021 - 12th International Conference on ICT Convergence
PB - IEEE Computer Society
T2 - 12th International Conference on Information and Communication Technology Convergence, ICTC 2021
Y2 - 20 October 2021 through 22 October 2021
ER -