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Range Sidelobe Suppression Approach for SAR Images Using Chaotic FM Signals.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Mar2022, Vol. 60, p1-15. 15p. - Publication Year :
- 2022
-
Abstract
- Range sidelobe is very common in synthetic aperture radar (SAR) images, particularly when imaging scene includes strongly scattering targets such as ships or complex buildings. As a kind of interference, it may reduce the image quality and hinder the image interpretation. Hence, range sidelobe suppression is an important mission for SAR images. The main task of mitigating the sidelobe is how to achieve the most effective suppression with the minimal resolution loss and signal-to-noise ratio (SNR) loss. However, the widely recognized classic method, spatially variant apodization (SVA), still has a lot of residual sidelobe energy and other problems. This article proposes a novel suppression approach based on time-variant transmission of chaotic frequency modulation (CFM) signals. The key is to build an appropriate transmitted signal set, where the signals are generated by various chaotic initial states and the same special map with low mixing rate and uniform invariant probability density (IPD). Due to their beneficial autocorrelation properties, the proposed approach achieves superior performance in range sidelobe suppression and resolution preservation. More importantly, it maintains the energy of the signals and overcomes the SNR loss that occurs in some classic methods, such as spectral weighting (SW) and SVA. In addition, it is suitable for both vertical and squint side-looking mode and can well reconstruct the weakly scattering targets which are severely disturbed by range sidelobe. All of them are validated by comparative experiments. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE analysis
*SYNTHETIC aperture radar
*SIGNAL-to-noise ratio
*APODIZATION
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 60
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 156372238
- Full Text :
- https://doi.org/10.1109/TGRS.2021.3137903