Modification of slant range model and imaging processing in GEO SAR

Geosynchronous (GEO) SAR is a radar satellite running in the inclinational geosynchronous orbit (36000km). During the long signal propagation delay time, the relative motion between satellite and target cannot be ignored. In this paper, the two-way slant range is analytically obtained from vector derivation. Meanwhile, it is found that the image formation must consider the non-uniform velocity in a long synthetic aperture time, and therefore, based on the derived slant range and the instantaneous velocity of satellite, a modified back projection algorithm is put forward to overcome the error of the “stop-and-go” error as well as the effects of the non-uniform satellite velocity. Simulation results validate the correctness of the improved imaging algorithm.

The Geosynchronous Satellite Synthetic Aperture Radar (GEO SAR) operates at a high orbital altitude, resulting in an extended imaging time and substantial variations in slant range. Additionally, the GEO SAR satellite orbit experiences a bending effect, and the target’s movement, caused by the Earth’s rotation, is influenced by the Earth’s curvature. The back-projection (BP) algorithm has been proven to be a highly effective technique for precise imaging with GEO SAR by processing these specific echo signals. However, this approach necessitates considerable computational resources. Existing BP algorithms, such as the fast BP algorithm, do not consider the “Stop-and-Go” error present in GEO SAR. Consequently, we developed a Precise Slant Range Model that considers the motion of both the satellite and targets. The model incorporates velocity and acceleration factors to accurately represent the signal transmission from transmission to reception. Additionally, we propose a Rapid Range History Construction Method to lessen the computational burden of generating the three-dimensional range history array. By utilizing the Precise Slant Range Model and the Rapid Range History Construction Method, and employing parallel processing through aperture segmentation, we propose an Accurate and Efficient BP imaging algorithm suitable for GEO SAR applications. To validate its effectiveness, simulations were conducted using the parameters of a GEO SAR system. The results indicated that the proposed algorithm enhances the imaging quality of GEO SAR, reduces the processing time, and achieves high-precision rapid imaging, thereby improving operational efficiency.

Geosynchronous Synthetic Aperture Radar (GEO SAR) is a possible next generation SAR system, which has the excellent performance of less than one-day revisit and hundreds of kilometres coverage. However, Radio Frequency Interference (RFI) is a serious problem, because the specified primary allocation frequencies are shared by the increasing number of microwave devices. More seriously, as the high orbit of GEO SAR makes the system have a very large imaging swath, the RFI signals all over the illuminated continent will interfere and deteriorate the GEO SAR signal. Aimed at the RFI impact in GEO SAR case, this paper focuses on the performance evaluation and the system design requirement of GEO SAR in the presence of RFI impact. Under the RFI impact, Signal-to-Interference-plus-Noise Ratio (SINR) and the required power are theoretically deduced both for the ground RFI and the bistatic scattering RFI cases. Based on the theoretical analysis, performance evaluations of the GEO SAR design examples in the presence of RFI are conducted. The results show that higher RFI intensity and lower working frequency will make the GEO SAR have a higher power requirement for compensating the RFI impact. Moreover, specular RFI bistatic scattering will give rise to the extremely serious impact on GEO SAR, which needs incredible power requirements for compensations. At last, real RFI signal behaviours and statistical analyses based on the SMOS satellite, Beidou-2 navigation satellite and Sentinel-1 A data have been given in the appendix.

Computerized Ionospheric Tomography (CIT) is a power tool for investigating the ionospheric electron density in three-dimensional space, and has important implications for weather forecast and scientific research. The existing researches on CIT are mostly based on the GPS data, which have disadvantages of complex operation, bad environment adaptability, small covering area and so on. The researches on CIT based on space-borne SAR are still at primarily stage and the relevant research methods are based on the low earth orbit SAR (LEO SAR) data, which have disadvantages of real-time performance and small covering area. This paper proposes a CIT technique based on geosynchronous SAR (GEO SAR), which may well solve the problems in the existing CIT techniques. In this paper, firstly, the advantages of CIT based on GEO SAR are analyzed. Then, the geometry and implementation process of CIT technique based on GEO SAR are given. Moreover, a GEO SAR satellite formation design is proposed in order to achieve better CIT performance. Finally, the simulation results of CIT based on GEO SAR using the International Reference Ionosphere (IRI) data are shown, verifying the theoretical analysis.

Geosynchronous synthetic aperture radar (GEO SAR) data focusing is a more challenging and difficult task than the low earth orbit (LEO) SAR due to the strong 2-D coupling of echo signal induced by the orbital trajectory curvature. The range cell migration (RCM) in the GEO SAR configuration is space variant in both range and azimuth directions, hence standard RCM correction (RCMC) functions developed for LEO SAR are inadequate for GEO. In this letter, a curved trajectory model is proposed, taking into consideration the impacts of "stop-and-go" assumption. Based on the range model, a new data transform is derived to deal with the complicated coupling in GEO SAR. From the derivation, we find that the original Stolt mapping is a special case of the proposed "generalized Omega-k" algorithm. In comparison with the original Omega-k algorithm, this new algorithm can correct more complicated RCM effectively. Finally, simulation results show that the proposed imaging algorithm performs well for large scene focusing in an L-Band GEO SAR system.

In the processing of high-resolution wide-swath spaceborne synthetic aperture radar data, the slant range history is difficult to formulate with a high accuracy over a long data-acquisition interval, and the variation of the imaging parameters over the illuminated scene makes it necessary to implement 2-D space-variant processing. To deal with these issues, first, a slant range expression is derived based on a geometry model using a circular arc to approximate the curved trajectory in this paper, which is composed of a hyperbolic term, a cubic term, and a quartic term. Then, the imaging flow based on the slant range model is proposed: a cubic filtering in range is employed to normalize the range-variant secondary range compression; the nonuniform fast Fourier transform (NUFFT) is used to correct the additional range displacement introduced by the cubic filtering and the nonlinear range cell migration (RCM) caused by the range dependence of the velocity; necessary modifications are made on the matched filter for azimuth compression to accommodate the cubic filtering in range and to deal with the high-order terms of the slant range model. The simulation results show that the proposed slant range model has a relatively high accuracy; the range processing procedure that involves an NUFFT following a cubic filtering can apply to the case where the nonlinear chirp scaling algorithm is adopted, and it has the advantage of being capable of dealing with the nonlinear RCM; the azimuth processing procedure can provide satisfactory focusing qualities over a rather large azimuth extent.

Geosynchronous SAR tomography (GEO TomoSAR) is a technique that combines geosynchronous SAR (GEO SAR) with SAR tomography. It can make full use of the advantages of short revisit time and large observation area of GEO SAR to achieve timely and accurate three-dimensional (3D) reconstruction of targets. However, GEO SAR is severely affected by the temporal-spatial variant ionosphere, which causes image shift and induces residual phase between repeated orbital data. These can seriously affect tomographic performance. In the paper, the GEO TomoSAR signal model affected by the ionosphere is established based on the temporal-spatial variant total electron content (TEC) model of background ionosphere. Then the influence of ionosphere on different scatterers projected into the same pixel in GEO SAR images is analyzed, and the expression of residual phase error between repeated orbit images is given. After analysis, the influence of the background ionosphere on the GEO TomoSAR system mainly includes the relative offset of the scattering target and the imaging defocus in the elevation direction. Finally, the experimental verification was completed using US-TEC data.

<p>Fast observations of rapid surface large-changes are demanded in disaster evaluations and scientific studies. Digital elevation model (DEM) differencing before and after the events is an effective way to retrieve the changes. Owing to a short repeat cycle, geosynchronous synthetic aperture radar (GEO SAR) systems can quickly obtain repeat-pass data and generate postevent DEMs by interferometry. However, interferometric baselines under its quick revisit cases are short, resulting in generating low-accuracy postevent DEMs. Moreover, surface large-changes can bring height ambiguity problems under the single-baseline interferometric processing. In this letter, we address the problem through a multibaseline (MB) processing. Since GEO SAR MB data can derive from the repeat-pass interferometric data of different subapertures and revisits, a subaperture-decomposition-based temporal and spatial MB method is proposed. The simulation results verify the effectiveness of the proposed method, where the quickly generated postevent DEM can help to realize the rapid large-elevation change observations. </p>

Because of the long integration time of geosynchronous earth orbit synthetic aperture radar (GEO SAR), the imaging model and algorithm of low earth orbit (LEO) SAR are not suitable for GEO SAR. Thus, this paper establishes the echo model of the GEO SAR by using the high order range model based on the characters of the GEO SAR movements. Then, the two-dimensional (2-D) spectrum is derived by the method of series reversion (MSR), based on which a new imaging algorithm used for GEO SAR is presented. Finally, simulation results of point targets prove the accuracy and validity of the proposed algorithm.

Geosynchronous SAR (GEO SAR) has great potentials in Earth observation as its wide swath coverage and fine resolution. But the long integration time is a double-edged sword which will lead to the error accumulations and the resultant performance degradation. In this paper, 3 demonstration experiments are presented for validating GEO SAR focusing feasibility and interferometry under long integration time, and the ionospheric influences. The GNSS satellites, including the Beidou IGSO and GPS satellites, are employed as illuminators. The principles, configurations, data processing and analysis are given for the 3 experiments separately. The experiment results validate well that GEO SAR can focus well and measure surface height with a good accuracy.

Geosynchronous orbit synthetic aperture radar (GEO SAR) plays an important role in wide-area surveillance and the continuous coverage of areas containing targets of interest. However, the relative position errors of large aperture arrays will distort the antenna pattern, which significantly degrades the target detection performance in a GEO SAR system. To address this issue, most of the conventional calibration methods are focused on the independent errors, without consideration of the parametric error model, which may increase the position estimation errors. To solve this problem, an efficient calibration algorithm for position errors in a GEO SAR is proposed in this letter. For the large antenna arrays, the parametric error model is first established. Then, the calibration method is performed to estimate the parameters of the position error model. Based on this, the accurate position errors can be obtained by the least-squares algorithm. Compared with the conventional methods, the target detection performance of a GEO SAR system can be significantly improved after the precise array position error compensation by the proposed algorithm. Moreover, the proposed algorithm transforms the estimation from 3-D positions to the finite parameters of the error model, which can considerably decrease the computational complexity and obtain a more accurate estimation of the position errors simultaneously. Several simulated results are presented to validate the proposed algorithm for the position error correction in a GEO SAR system.

Multiple‐input multiple‐output geosynchronous synthetic aperture radar (MIMO GEO SAR), composed by several mini‐satellites in GEO orbits, has more spatial degrees of freedom, which helps to suppress clutter and improves the performance of ground moving target indication (GMTI). However, in order to ensure the satellite safety, the satellite spacing in MIMO GEO SAR is very large. It results in the failure of far‐field assumption and causes the moving target defocusing because of the mismatch between the steering vector in far‐field sources and the moving target echo. Here, a new signal model based on the near‐field sources is first given according to the GEO SAR satellite‐ground geometry relationship. Then, considering the phase difference between the different channels, the spatial steering vector is derived and the weight vector for adaptive processing is proposed to suppress clutter and indicate the moving target. Finally, computer simulations are carried out for the validation of the proposed method.

Synthetic aperture radar on geosynchronous orbit (GEOSAR) can realize the continuous image on the larger coverage area in the short revisit period of nearly 24h. The four-order slant range model based on the “stop-and-go” supposition for the curve track has been proposed in the some literatures. Owing to the high attitude and the wide swath of GEOSAR, effect of the signal propagated delay and range displacement of point target on the slant range model can lead to the defocused imaging results. Hence, in this paper, we firstly infer the advance slant range model using Taylor expansion considering to the path delay and the position of point target. Then, we deduce the point target two dimensional frequency spectrum and the improved CS imaging method based the above range model. Finally, the correctness of the CS imaging method based on the improved slant range model is verified by the simulation.

Geosynchronous SAR (GEO SAR) has better coverage and revisiting performance than the low Earth orbit SAR. Thus GEO SAR can implement the repeat-pass interferometry as its revisit time of 1 day and the orbit drift (forming baseline) induced by perturbation. In comparison, the twin GEO SAR formation configuration in which the slave satellite repeats the master satellite' track can realize shorter revisit time and adjustable baseline by tuning the orbit elements. In this paper, a validation experiment based on the Chinese Beidou IGSO navigation satellites is demonstrated to validate the feasibility and effectiveness of this concept. The validation experiment consists of data acquisition of the Beidou IGSO satellites signals, equivalent pre-processing, image processing and interferometry processing. In the end, we obtain the high-precision experimental results of deformation inversion, validating the feasibility of twin GEO SAR interferometry/differential interferometry.

The ability of differential interferometric synthetic aperture radar (DInSAR) technology used for centimeter level deformation detection has been well-proved. Nevertheless, the applications of DInSAR under the low Earth orbit (LEO) are limited to the line-of-sight (LOS) measurements, the limited observation area and the long revisited time. Geosynchronous SAR (GEO SAR), which runs in the height of 36000 km with the advantage of a short revisit time and a large observation area, is a potential approach to overcome the series problems in LEO SAR. This paper focuses on estimating three-dimensional (3D) displacements by using GEO SAR DInSAR measurements acquired from multiple imaging geometries. Aiming to provide a normal solution for geologic hazard monitoring and relative geophysical application in the future, the errors induced by decorrelation noise, orbital ramp and ionospheric distortion are analyzed for the GEO SAR DInSAR measurements. A series of experiments have been conducted to find the relationship between the 3D displacements and the DInSAR observations which are provided with different noises and combinations of imaging geometries. The results reveal that 3D displacements can be expected from the combination of left- and right-looking GEO SAR DInSAR measurements. In particular, the north-south solution can achieve centimeter and even millimeter level.