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| Citation: | LI Hang, LIU Wenkang, SUN Guangcai, et al. MEO SAR imaging based on imaging coordinate system optimization[J]. Journal of Radars, 2020, 9(5): 856–864. doi: 10.12000/JR20098
|
MEO SAR Imaging Based on Imaging Coordinate System Optimization
DOI: 10.12000/JR20098
More Information-
Abstract
In the Medium-Earth-Orbit Synthetic Aperture Radar (MEO SAR), the curved trajectory and long synthetic aperture time lead to a two-dimensional spatial variation in the signals. Traditional methods usually process the range and azimuth variations separately, and the computational complexities are high. Herein, we study the Doppler rate distribution across a large scene and propose a non-orthogonal and nonlinear coordinate system wherein the MEO SAR signals satisfy the azimuth-shift–invariant property. Thus, the efficiency of the image formation processor can be significantly improved. The higher-order Doppler parameters are addressed by the Doppler linearization. Then, more precise focusing can be achieved, and the azimuth time-shift caused by the changes in signal distribution is addressed. Finally, the processing results of simulated stripmap-mode data with a 2-m resolution are presented to validate the effectiveness of the proposed algorithm. -
References
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LI Zhi, TANG Chengyao, DAI Yongpeng, et al. Multirotor UAV-borne vital signs sensing using 4D imaging radar[J]. Journal of Radars, 2025, 14(1): 62–72. doi: 10.12000/JR24128
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- Figure 1. MEO SAR imaging geometry
- Figure 2. Doppler rate plane
- Figure 3. Flowchart of the proposed imaging algorithm
- Figure 4. Arrangement of simulated targets
- Figure 5. Relationships between Doppler rates and ranges of the simulated targets
- Figure 6. Simulation results using the NCS algorithm in Ref. [22]
- Figure 7. Simulation results using the JTDR algorithm in Ref. [21]
- Figure 8. Simulation results using the proposed method
- Figure 9. Computation comparison using different methods