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OTHR multitarget tracking with a GMRF model of ionospheric parameters.

Authors :
Guo, Zhen
Wang, Zengfu
Lan, Hua
Pan, Quan
Lu, Kun
Source :
Signal Processing. May2021, Vol. 182, pN.PAG-N.PAG. 1p.
Publication Year :
2021

Abstract

• Utilizing spatial correlation of ionosphere is important in OTHR target tracking. • GMRF represents ionospheric parameters more accurately for OTHR target tracking. • Joint estimation and decision using both OTHR and ionosondes measurements helps. The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, namely ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01651684
Volume :
182
Database :
Academic Search Index
Journal :
Signal Processing
Publication Type :
Academic Journal
Accession number :
148543877
Full Text :
https://doi.org/10.1016/j.sigpro.2020.107940