Open AccessFull polarisation ISAR imaging based on joint sparse Bayesian compressive sensing
Author(s) -
Gu Yalong,
Pei Chunying,
Wang Xin,
Chen Rushan,
Tao Shifei
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0365
Subject(s) - compressed sensing , inverse synthetic aperture radar , computer science , bayesian probability , joint (building) , channel (broadcasting) , bayesian inference , artificial intelligence , synthetic aperture radar , radar imaging , computer vision , inverse problem , radar , algorithm , pattern recognition (psychology) , mathematics , telecommunications , engineering , architectural engineering , mathematical analysis
This study proposes a joint sparse algorithm based on Bayesian compressive sensing to improve full polarisation inverse synthetic aperture radar (ISAR) imaging performance. The proposed method not only uses the sparseness of each single channel polarisation, but also takes into account the correlation of amplitude information between single‐polarised channels. Through the comprehensive use of single channel polarisation imaging results, a better full‐polarisation imaging result is achieved. Simulation results are used to verify the effectiveness of the method.