A novel sidelobe-suppression algorithm for airborne synthetic aperture imaging ladar. (April 2019)

• Record Type: Journal Article
• Authors: Guo, Liang; Yin, Hongfei; Zhou, Yu; Sun, Jianfeng; Zeng, Xiaodong; Tang, Yu; Xing, Mengdao
• Is Part Of: Optics & laser technology. Volume 111(2019)
• Page Start: 714
• Page End: 719
• Publication Date: 2019-04
• Subjects: Synthetic Aperture Ladar (SAL) -- Compression Sensing (CS) -- Modified Spatial Variant Apodization (SVA) -- Sparse -- Sidelobe-suppression; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2018.09.005
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: A sidelobe-suppression method based on modified SVA and CS is proposed. The SAL images is converted into sparse by using the modified SVA algorithm. Compression Sensing theory is used to suppress the sidelobes of the SAL signal. Abstract: A novel sidelobe-suppression algorithm is proposed according to the problem of sidelobe in the real data of airborne Synthetic Aperture Ladar (SAL) imaging. Compression Sensing (CS) theory is proposed to recover a sparse signal as well as to suppress the sidelobe of the signal. However, the SAL images are not sparse in a general way. To solve such a problem, a novel sidelobe-suppression algorithm based on modified Spatial Variant Apodization (SVA) technique connected with CS theory is proposed. Firstly, the sparsification of SAL images can be achieved by using the modified SVA algorithm, which deals with not only the linear but also the non-linear sampling situations. And then, the sidelobe-suppression can be achieved by recovery processing using the CS algorithm. Finally, the simulation shows that the sidelobes of the signal can be suppressed as low as −34 dB without losing resolution. Meanwhile, the real data result proves the validity of the proposed algorithm.