Sparse Bayesian Learning Based Three-Dimensional Imaging for Antenna Array Radar
Yuhan Li, Jesper Rindom Jensen, Maozhong Fu, Zhenmiao Deng, Mads Græsbøll Christensen
Abstract
In recent years, the development of compressed sensing and sparse representation provide us with a broader perspective of three-dimensional (3-D) imaging. In this work, we propose a 3-D imaging method based on a sparse Bayesian learning(SBL) framework for antenna array radar. It solves the problem of long-term accumulation and complicated motion compensation problem that occurs with interferometric inverse synthetic aperture radar (InISAR). Using the framework, the proposed method can automatically learn optimal hyper-parameters from the data at a low computational cost. Experimental results show that the proposed method has advantages in terms of 3-D imaging accuracy and computational efficiency compared to existing methods.
BibTeX
@inproceedings{icassp2023_sparsebayesianle,
title = {Sparse Bayesian Learning Based Three-Dimensional Imaging for Antenna Array Radar},
author = {Yuhan Li and Jesper Rindom Jensen and Maozhong Fu and Zhenmiao Deng and Mads Græsbøll Christensen},
booktitle = {ICASSP 2023},
year = {2023}
}