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Geosynchronous Synthetic Aperture Radar (GEO SAR) has a very long Coherent Processing Interval (in the order of hundreds of seconds) compared with other SAR platforms. Thus, the current methods of rotation effect matching and ship imaging that operate within a relatively short Coherent Processing Interval (in the order of seconds) are obviously not applicable. To address this problem, a novel ship imaging method with multiple sinusoidal functions matching for rotation effects is proposed for GEO SAR. Firstly, the influence of the rotational motion of a ship on the slant range is analyzed. It can be matched with the sum of multiple sinusoidal functions, and the signal model of a ship with rotational motion is given. Then, multiple sinusoidal functions for the matching-based ship imaging method are proposed, and their procedures are presented as follows: (1) The Generalized Keystone Transform and Generalized Dechirp Process (GKTGDP) is modified to compensate for the range migration and phase caused by the motion of GEO SAR. Then, the signal is focused at the frequencies of sinusoidal functions, and the frequencies can be matched. (2) From the matched frequencies, the other parameters of sinusoidal functions can be matched by parameter searching. (3) Based on the matched results, the Back Projection Algorithm (BPA) is used to take an image of the ship with rotational motion. Finally, the effectiveness of the proposed method is verified by numerical experiments.

An L band geosynchronous synthetic aperture radar (GEO SAR) differential interferometry system (D-InSAR) will be obviously impacted by the background ionosphere, which will give rise to relative image shifts and decorrelations of the SAR interferometry (InSAR) pair, and induce the interferometric phase screen errors in interferograms. However, the background ionosphere varies within the long integration time (hundreds to thousands of seconds) and the extensive imaging scene (1000 km levels) of GEO SAR. As a result, the conventional temporal-spatial invariant background ionosphere model (i.e., frozen model) used in Low Earth Orbit (LEO) SAR is no longer valid. To address the issue, we firstly construct a temporal-spatial background ionosphere variation model, and then theoretically analyze its impacts, including relative image shifts and the decorrelation of the GEO InSAR pair, and the interferometric phase screen errors, on the repeat-track GEO D-InSAR processing. The related impacts highly depend on the background ionosphere parameters (constant total electron content (TEC) component, and the temporal first-order and the temporal second-order derivatives of TEC with respect to the azimuth time), signal bandwidth, and integration time. Finally, the background ionosphere data at Isla Guadalupe Island (29.02 • N, 118.27 • W) on 7-8 October 2013 is employed for validating the aforementioned analysis. Under the selected background ionosphere dataset, the temporal-spatial background ionosphere variation can give rise to a relative azimuth shift of dozens of meters at most, and even the complete decorrelation in the InSAR pair. Moreover, the produced interferometric phase screen error corresponds to a deformation measurement error of more than 0.2 m at most, even in a not severely impacted area.

A geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO SA-BSAR) system is an important technique to achieve long-time moving target monitoring over a wide area. However, due to special bistatic configuration of GEO SA-BSAR, two major challenges, i.e., severe range migration and space-variant Doppler parameters for moving targets, hinder the moving target indication (MTI) processing. Traditional SAR MTI methods, which do not take the challenges into consideration, will defocus the moving targets, leading to a loss of the signal-to-noise ratio (SNR). To focus moving targets and estimate motion parameters accurately, long-time coherent integration space-time adaptive processing (LTCI-STAP) is proposed for GEO SA-BSAR MTI in this paper. First, a modified adaptive spatial filtering based on the bistatic signal model is performed to suppress the clutter. Then, an LTCI filter bank is constructed to achieve range migration correction and moving target focusing, which yields the optimal output signal and filtering parameters. Finally, constant false alarm rate (CFAR) detection is carried out to determine the targets, and the space-variant Doppler parameters, solved from the filtering parameters, are used for estimating moving target positions and velocities. Simulations verify the effectiveness of our method.