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An Advanced Nonlinear Frequency Modulation Waveform for Radar Imaging With Low Sidelobe.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Aug2019, Vol. 57 Issue 8, p6155-6168, 14p
- Publication Year :
- 2019
-
Abstract
- With the development of high-resolution radar satellite for global comprehensive environmental monitoring, day-and-night and all-weather surveillance has become an active and growing research field. However, in all cases, these applications require radar to have a high-efficiency radar module (e.g., T/R module), and high system transmitting power. These requirements may put an important limitation on the performance of a radar satellite with a high-power configuration. In this paper, we report a novel waveform optimization framework. Through this framework, an advanced nonlinear frequency modulation (NLFM) waveform with lower sidelobes and a smaller main lobe, which can significantly relieve the restriction of very limited satellite power, is constructed. In addition, we apply it in a real synthetic aperture radar (SAR) system with a bandwidth of 100 MHz at 9.6-GHz carrier frequency and the whole process of the NLFM waveform for radar imaging is discussed in detail, including the system architecture and configuration, a system error compensation method, and a modified chirp scaling algorithm (CSA). The imaging results demonstrate the excellent performance of the advanced NLFM waveform. Moreover, we observe that the SAR system with the advanced waveform has a higher signal-to-noise ratio (SNR) of 1.29 dB compared with the conventional linear frequency modulation (LFM) waveform. The improvement of 1.29-dB SNR means that the real radar system can reduce transmitting power with a ratio of 25%. This effect is likely to be a potential feature of NLFM waveform, which can reduce the transmitting power requirement, especially for radar satellite. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 57
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 138462773
- Full Text :
- https://doi.org/10.1109/TGRS.2019.2904627