Open AccessSparse‐Bayesian‐learning‐based translational motion estimation of electromagnetic vortex imaging
Author(s) -
Li Rui,
Ma Zhiqiang,
Zhang Qun,
Luo Ying,
Liang Bishuai,
Li Guangming
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0667
Subject(s) - vortex , computer science , algorithm , artificial intelligence , parametric statistics , computer vision , physics , focus (optics) , bayesian probability , motion (physics) , signal (programming language) , motion estimation , optics , mathematics , statistics , thermodynamics , programming language
The electromagnetic (EM) vortex imaging has been found to have a great potential application prospect in the imaging radar field. However, current studies focus on the motionless target, which seriously limits its application in practice. Therefore, to achieve EM vortex imaging for the motion target, this study proposes a parametric sparse representation model for EM vortex imaging that takes into account a translational motion target and uses the stepped frequency signal. An iterative algorithm is developed based on the sparse Bayesian learning (SBL) algorithm to estimate the velocity, and accomplish the EM vortex imaging exploiting SBL algorithm. Simulation results demonstrate that the proposed algorithm can improve velocity estimate accuracy in terms of relative error and achieve EM vortex imaging for the motion target.