Sparse-Bayesian-learning-based translational motion estimation of electromagnetic vortex imaging

Abstract

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.

Keywords

• bayes methods
• learning (artificial intelligence)
• motion estimation
• radar imaging
• iterative methods
• translational motion target
• sparse bayesian learning algorithm
• em vortex imaging
• sparse-bayesian-learning-based translational motion estimation
• electromagnetic vortex imaging
• great potential application prospect
• imaging radar field
• parametric sparse representation model