Abstract
Due to geosynchronous synthetic aperture radar’s (GEO SAR) high orbit and low relative speed, the integration time reaches up to hundreds of seconds for a fine resolution. The short revisit cycle is essential for remote sensing applications such as disaster monitoring and vegetation measurements. Three-dimensional (3D) scene imaging mode is crucial for long-term observation using GEO SAR. However, this mode will bring a new kind of space-variant error in elevation. In this paper, we focus on the analysis of the elevation space-variant error. First, the decorrelation problems caused by the spatial variation are presented. Second, by combining with the SAR imaging geometry, the elevation spatial variation is decomposed into two-dimensional (2D) space variation of range and azimuth. Third, an imaging algorithm is proposed to solve the 3D space variation and improve the focusing depth. Finally, simulations with dot-matrix targets and distributed targets are performed to validate the imaging method.
Highlights
Spaceborne synthetic aperture radar (SAR) is a radar system for ground observation with satellite as the carrier
In the imaging processing of the geosynchronous synthetic aperture radar (GEO SAR) system, since the orbit of the satellite is elliptical during synthetic aperture time, the classical second-order slant range model is no longer applicable, and an accurate slant range model must be adopted, which means that the expression will be more complicated
This paper mainly studies the influence and compensation algorithm of elevation spatial variation
Summary
Spaceborne synthetic aperture radar (SAR) is a radar system for ground observation with satellite as the carrier. For a target with a certain elevation in the GEO SAR system, due to the asymmetric trajectory of the satellite in the synthetic aperture time, the corresponding points that have exactly the same range history will not be found, which will cause the azimuth defocus of the imaging results [9]. Some people have proposed the high-order Taylor expansion slant range model in the GEO SAR imaging algorithm [19,20,21], but it is only for the imaging of point targets and does not consider the elevation space variation. Based on the previous research results, this paper comprehensively considers the use of high-order slant range model in the compensation of space-variant error caused by elevation The coefficients of this model are related to range, azimuth and elevation.
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