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.

The Geosynchronous-Low-Earth-Orbit bistatic synthetic aperture radar (GEO-LEO BiSAR) adopts the transceiver split system form, in which GEO SAR serves as the illuminator and LEO SAR serves as the receiver. GEO-LEO BiSAR can obtain abundant target scattering information, can realize large width imaging and reduce the receiver cost. Three parameters are selected as measurement indicators to comprehensively evaluate system performance and provide system design guidance. The spatial resolution can measure the imaging performance, the radiation resolution is the ability to distinguish the objects scattering characteristics, and the beam coverage area represents the system observation ability. They are all key parameters and are closely related to the BiSAR configuration. In this paper, we use the generalized ambiguity function (GAF) and integral equation model (IEM) to derive the BiSAR spatial and radiation resolution expressions, and the beam coverage area calculation method is derived through geometric knowledge. These three parameters are modeled as objective functions for the system design of the multi-objective optimization problem (MOP), in which multi-objective evolutionary algorithm based on decomposition and differential evolution (MOEAD-DE) is used to solve the receiver orbital element. We can optimize these three parameters jointly considering their values or optimize the resolution (spatial and radiation resolution) and coverage area in sequence. Finally, the analysis correctness is verified by simulation experiments. The proposed system design method comprehensively considers the ability of observation, resolution and recognition, and can reasonably select joint or sequential optimization schemes according to the system capability requirements to guide the receiver orbital element selection.

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.

Geosynchronous Synthetic Aperture Radar (GEO SAR) has important potential applications in the fields of atmospheric water cycle and digital weather forecast, and establishing the connection between GEO SAR imaging and atmospheric water vapor inversion is a key step. The turbulent variation of atmospheric phase delay will cause the SAR image defocus, and turbulent tropospheric delay phase estimation based on block autofocus method in low-orbit high resolution space-borne SAR can be used for reference, but this method is very sensitive to the data block selection, modulation frequency (FM) outlier detection and correction. This Letter gives some considerations to these two problems of block selection and outlier correction. Based on the idea of Extreme Learning Machine (ELM), a mathematical model with nonlinearity as the core and linear model as easy to solve is established. When the parameters affecting the turbulent troposphere are known, the model can be directly used to obtain the block optimization selection, namely, the number and size of blocks. When the parameters are unknown, the model can be used to roughly inverse the parameters and provide initial values for iteration. For the second problem, using the prior information of the correlation of the FM estimated by the adjacent sub-blocks, we proposes a Local Outlier Factor (LOF) method to identify the error point by defining the distance threshold. The effectiveness of the proposed algorithm is verified by Sentinel-1 data injected with tropospheric error.

Geosynchronous synthetic aperture radar (GEO SAR) operating in squint mode has the advantages of wide coverage and short revisit, which can improve the ability of emergency response and continuous observation compared with broadside mode. Due to the sensitivity of GEO SAR to squint angle, a large amount of data redundancy and severe 2-D spatial variation problems are encountered in imaging processing. By utilizing the variable pulse repetition interval (VPRI) technique, the data reception window is adapted to accommodate changes in range cell walk, thereby eliminating redundant data. The current frequency domain imaging algorithm is not suitable for non-uniformly sampled data. This paper proposes a method based on non-uniform fast Fourier transform (NUFFT) and modified range Doppler azimuth chirp scaling (RD-ACS) algorithm for processing squint GEO SAR data. Azimuth NUFFT can obtain data with equidistant Doppler frequencies. A 2-D frequency spectrum of reference points within the scene is used for bulk phase compensation. The slant range is represented by a high-order polynomial containing 2-D spatial variations. During range cell migration correction and azimuth compression process, the varying coefficients of slant range with range are used to replace the corresponding reference point coefficients for range space-variant compensation. The azimuth spatial variation is addressed by the CS method, and the phase introduced by the scaling equation is compensated and the azimuth compression is completed in the RD domain. The computational complexity of the algorithm is discussed. Finally, the correctness of the proposed algorithm is verified by simulation experiments.

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.