Geosynchronous SAR: System and Signal Processing

Radar is an electronic device that uses electromagnetic waves to detect targets. Early radar systems used time delays to measure the distance between the radar and the target, and they determined the direction of the target through the antenna pointing, and then used the Doppler shift to detect target velocity.

GEO SAR is a new type of space-borne SAR, which runs on the geosynchronous orbit and has the advantages of short revisit time and wide coverage. However, due to the high geosynchronous orbit, the system characteristics and design methods of GEO SAR are quite different from those of the traditional low-earth-orbit SAR (LEO SAR), which are introduced in this chapter. Firstly, the system characteristics of GEO SAR are analyzed. The differences between GEO SAR and LEO SAR are introduced briefly, and then the coverage and revisit abilities are compared with the traditional LEO SAR. After that, a curvature circle motion model (CMM) is introduced to describe the curved satellite track, the Doppler characteristics are analyzed and the work modes are compared. Secondly, the parameter design methods of resolution, power and ambiguity are presented for GEO SAR, considering the high squint observation. Thirdly, two attitude steering strategies are introduced for GEO SAR. The total zero Doppler steering (TZDS) method aims to compensate the serious effects of Earth rotation and obtain a zero Doppler centroid, and the optimized resolution steering (ORS) method attempts to reduce the required steering angle and obtain an optimal resolution feature.

GEO SAR imaging processing faces serious difficulties because of the negative influence caused by high orbital altitude, curved trajectory, long synthetic aperture time (SAT) and large scene size, including complex slant range history, failure of “stop-and-go” assumption, two-dimensional (2D) spatially variant slant range model coefficients, etc. To solve these problems and achieve GEO SAR imaging, an accurate echo signal model, including the slant range model, the 2D spectrum and the spatially variant model of slant range coefficients based on the curved trajectory and Taylor series which take the error of “stop-and-go” assumption into account are first introduced in detail. Then, the time domain algorithm which can be used in any conditions is discussed detailedly, including its procedure flows and computational load. To improve the processing efficiency, the frequency domain algorithm is addressed. In this chapter, the difficulties of GEO SAR imaging in frequency domain are first analyzed in detail; then, the azimuth compensation which consists of a time domain compensation and a frequency domain compensation is derived to reduce the azimuth variance of the focus parameters and unfold the folded azimuth spectrum at certain specific orbital positions; after the azimuth compensation, a 2D nonlinear chirp scaling algorithm (NCSA) is introduced to finally obtain GEO SAR images; besides, the accuracy of the frequency algorithm, including its geometric distortion and available azimuth swath is discussed. At last, we summarize this chapter and give the conclusion.

Due to the ultra-long integration time and large coverage characteristics of geosynchronous synthetic aperture radar (GEO SAR), the atmospheric frozen model in the traditional low Earth orbit SAR (LEO SAR) imaging fails in GEO SAR. The temporal-spatial variation of troposphere and ionosphere should be taken into account for the GEO SAR imaging. Based on the accurate GEO SAR signal model, the two-dimensional spectrum of GEO SAR signal in the context of temporal-spatial variant troposphere and background ionosphere are derived, and then the two-dimensional image shift and defocusing are investigated. The boundary conditions of relevant effects are analyzed and summarized which are related to the status of troposphere and background ionosphere, the GEO SAR imaging geometry and the integration time dependent on the resolution requirement. GEO SAR is also sensitive to ionospheric scintillation which causes the amplitude and phase fluctuations of signals. The corresponding degradation will have a different pattern from LEO SAR. The azimuth point spread function considering the scintillation sampling model is constructed. Then, based on the measurable statistical parameters of ionospheric scintillation, performance is quantitatively analyzed. The analysis suggests that in GEO SAR imaging the azimuth integrated side lobe ratio deteriorates severely, while the degradations of the azimuth resolution and azimuth peak-to-sidelobe ratio are negligible when scintillation occurs.

The L-band Geosynchronous SAR (GEO SAR) is very susceptible to the temporal-spatial variant ionosphere as its long integration time and large coverage, leading to image drift and degradation. This chapter demonstrates an experimental study of ionospheric influences on GEO SAR, including both the background ionosphere and the ionospheric scintillation. In the experiment, we employ the Global Positioning Satellites (GPS), probe the ionosphere and collect the trans-ionosphere GPS signals. Then the recorded signals are used to create the data basis on which simulations and analysis are based. But GEO SAR has very different orbit trajectories from GPS. Thus in the real operation, the transformation of the temporal-spatial frame between GPS and GEO SAR should be first performed before the focusing and the evaluation are carried out. Then the influences of the background ionosphere and the ionospheric scintillation are analyzed based on the experimental data. Finally, the corresponding compensation or mitigation approaches are presented. For the temporal-spatial variant background ionosphere, the autofocus method can produce a well-focused image. In comparison, the random fluctuations of amplitude and phase induced by ionospheric scintillation are more difficult to deal with. Thus an orbit-optimization strategy is first proposed by utilizing the diurnal and geographical pattern of the ionospheric scintillation occurrence. It can avoid being interfered by ionospheric scintillation by tuning the orbit parameters. Alternatively, once interfered, an iterative algorithm based on entropy minimum is derived to jointly compensate the signal amplitude and phase fluctuations in GEO SAR.

Geosynchronous SAR has almost the same trajectory during its orbit period which makes it suitable for interferometric SAR and differential interferometric SAR (InSAR and D-InSAR) processing. But the very large orbit height, which is about 60 times larger than that in a low Earth orbit SAR, will cause lots of special issues in the repeated-track InSAR and D-InSAR. In this chapter, we firstly analyze the effects of the un-parallel repeated tracks and the squint-looking working mode introduced by the orbital perturbations and the Earth’s rotation in the repeat-track GEO InSAR system. Then, a novel data acquisition method is presented based on a criterion of optimal minimal rotational-induced decorrelation (OMRD). It can significantly improve the coherence of the InSAR pair. In the meantime, a modified GEO InSAR height retrieval model is proposed to mitigate the height and localization errors induced by the conventional model. Moreover, we also introduce the processing steps in GEO InSAR and D-InSAR. The retrieved height and deformation results are shown for validating the good performances of GEO InSAR system. At last, both the height retrieval accuracy and the deformation retrieval accuracy are analyzed detailedly with the consideration of the variations of the baseline lengths and the orbital configurations.

GEO SAR has characteristics of short revisit time of less than one day, extended coverage area with even larger than 1000 km and long coverage time of several hours for the scene of interest, and thus can provide data of a certain region of interest with lots of view angles. Consequently, employing GEO SAR for three-dimensional (3D) deformation retrieval can effectively address the drawbacks in LEO SAR cases, which are the lack of available data and the limited deformation retrieval accuracy. In this chapter, we first give some brief explanation about the reason why we should conduct 3D deformation retrieval instead of the simple one-dimensional (1D) line-of-sight (LOS) deformation measurement. Then, we focus on the GEO SAR 3D deformation retrieval by multi-angle measurement. To obtain the optimal accuracy, we consider the reasonable criterion to evaluate the 3D deformation measurement accuracy and implement it for optimal sub-aperture selection in 3D deformation retrieval.