Open AccessSynthetic Aperture Radar Tomography in Urban Area Based on Compressive MUSIC
Author(s) -
Hao Li,
Jian Yin,
Shuang Jin,
Daiyin Zhu,
Wen Hong,
Hui Bu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012055
Subject(s) - compressed sensing , synthetic aperture radar , computer science , tomography , inverse synthetic aperture radar , radar imaging , artificial intelligence , radar , elevation (ballistics) , computer vision , signal (programming language) , remote sensing , geology , mathematics , optics , physics , telecommunications , geometry , programming language
Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation for three-dimensional (3-D) imaging. Spectral estimation is the conventional TomoSAR imaging method, such as multiple signal classification (MUSIC). For the sparse elevation distribution, compressive sensing (CS) is a favorable technique for the elevation reconstruction. Compressive sensing multiple signal classification algorithm (CS-MUSIC) is a combination of CS and MUSIC algorithm. It takes the advantage of both conventional spectral estimation and CS technology, and hence overcomes the drawbacks of existing methods and obtains the super-resolution ability. In this paper, the effectiveness of CS-MUSIC in TomoSAR imaging of urban area has been validated by simulated and real TerraSAR-X data. CS-MUSIC algorithm can effectively solve the problem of acquiring high-resolution TomoSAR imaging with small amount of data.