Coherence-based synthetic aperture radar (SAR) tomography (TomoSAR) exploits the complex coherences of SAR images to achieve 3-D imaging. Utilizing two-sensor spaceborne SAR formation flying to realize coherence-based TomoSAR has attracted increasing attention because temporal decorrelation-free interferograms can be constructed; therefore, TomoSAR has excellent potential for inverting the vertical structures of natural scenes such as forests and glaciers. However, low earth orbit (LEO) TomoSAR is disadvantaged by limited data and nonuniform sampling in the elevation direction. Geosynchronous (GEO) TomoSAR can overcome these limitations owing to its short revisit time of no more than 24 h. For the first time, this article discusses coherence-based TomoSAR exploiting GEO SAR formation flying. The benefits of GEO-formation coherence-based TomoSAR, including the low cost of slave satellites, rich data sets, and uniform sampling, are noted. The key problems of system design, including the formation design and data acquisition, are discussed. A formation design method based on the minimum along-track baseline is proposed that can realize uniform elevation sampling. The geometric correlation of a general SAR observation geometry is derived; on this basis, an optimal data acquisition method based on the optimal height measurement Cramer-Rao lower bound (CRLB) is proposed. Finally, the performance of GEO-formation coherence-based TomoSAR is analyzed; in particular, the ambiguity height in the altitude direction, the Rayleigh resolution in the altitude direction, and the theoretical optimal geometric correlation are evaluated. Finally, computer simulations validate the proposed formation design method, data acquisition scheme, and performance analysis formula.

With the development trends of multistatic spaceborne synthetic aperture radar (SAR), geosynchronous SAR (GEO SAR) employing several formation-flying small satellites also has great potential for remote sensing. The small satellites can cooperate to acquire multi-channel data for moving target detection and parameter estimation in strong clutters. However, multistatic GEO SAR has large satellite spacing and a curved trajectory, which induce the near-field effects and channels out of alignment, respectively, bringing about challenges for the spatial adaptive processing. These problems produce a high-order term in the multi-channel slant range model, making the traditional model and adaptive processing method invalid. In this paper, to meet the requirement of SAR focusing, we firstly derive a fourth-order slant range model and a third-order path difference model for multistatic GEO SAR. Secondly, based on the derived model, the principle of stationary phase and series reversion method are utilized to derive the spatial steering vector for a moving target, which is a basis of spatial adaptive processing in the range-Doppler domain. Thirdly, the time-domain match filtering is constructed based on the fourth-order slant range model to image the moving target. Additionally, the moving targets are detected in the image domain. The motion parameter is estimated by iteratively maximizing the output signal to clutter and noise ratio (SCNR) through the range of possible target velocities. Finally, considering that the GEO SAR is still in development, the computer simulations are carried out to verify the effectiveness and evaluate the performance.