Dexin Li

The zero-Doppler centroid control in geosynchronous synthetic aperture radar (GEO SAR) is beneficial to reduce the imaging complexity (reduces range-azimuth coupling in received data), which can be realized by adjusting the radar line of sight (RLS). In order to maintain the zero-Doppler centroid throughout the whole orbit of the GEO SAR satellite, the RLS needs to be adjusted in real-time. Due to the ultra-long synthetic aperture time of GEO SAR, the RLS variation during the synthetic aperture time cannot be neglected. However, in the previous related papers, the real-time variation of RLS during the synthetic aperture time was not taken into account in the calculation of Doppler parameters, which are closely related to the RLS, resulting in inaccurate calculation of Doppler parameters. Considering this issue, an accurate Doppler model (the model of relative motion between satellite and ground target) of GEO SAR is proposed in this paper for the accurate calculation of Doppler parameters (Doppler centroid and Doppler bandwidth and other parameters). Finally, simulation experiments are designed to confirm the effectiveness and necessity of the proposed model. The results indicate that the RLS variation during the synthetic aperture time has a considerable effect on Doppler parameters performance of the GEO SAR, and refers to a more stable azimuth resolution performance (the resolution is kept near a relatively stable value at most positions of the elliptical orbit) compared with the case that does not consider the real-time zero-Doppler centroid control.

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion.

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.