Modeling and Analysis of the Impacts of Temporal-Spatial Variant Troposphere on Ground-Based SAR Imaging of Asteroids

Journal of Beijing Institute of Technology ›› 2023, Vol. 32 ›› Issue (6) : 727 -739.

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Journal of Beijing Institute of Technology ›› 2023, Vol. 32 ›› Issue (6) : 727 -739. DOI: 10.15918/j.jbit1004-0579.2023.095

Modeling and Analysis of the Impacts of Temporal-Spatial Variant Troposphere on Ground-Based SAR Imaging of Asteroids

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Abstract

The near-Earth asteroid collisions could cause catastrophic disasters to humanity and the Earth, so it is crucial to monitor asteroids. Ground-based synthetic aperture radar (SAR) is an observation technique for high resolution imaging of asteroids. The ground-based SAR requires a long integration time to achieve a large synthetic aperture, and the echo signal will be seriously affected by temporal-spatial variant troposphere. Traditional spatiotemporal freezing tropospheric models are ineffective. To cope with this, this paper models and analyses the impacts of temporal-spatial variant troposphere on ground-based SAR imaging of asteroids. For the background troposphere, a temporal-spatial variant ray tracing method is proposed to trace the 4D (3D spatial + temporal) refractive index network provided by the numerical weather model, and calculate the error of the background troposphere. For the tropospheric turbulence, the Andrew power spectral model is used in conjunction with multiphase screen theory, and varying errors are obtained by tracking the changing position of the pierce point on the phase screen. Through simulation, the impact of temporal-spatial variant tropospheric errors on image quality is analyzed, and the simulation results show that the X-band echo signal is seriously affected by the troposphere and the echo signal must be compensated.

Keywords

near-Earth asteroids / ground-based SAR / troposphere / ray tracing

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null. Modeling and Analysis of the Impacts of Temporal-Spatial Variant Troposphere on Ground-Based SAR Imaging of Asteroids. Journal of Beijing Institute of Technology, 2023, 32(6): 727-739 DOI:10.15918/j.jbit1004-0579.2023.095

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