Computerized ionospheric tomography based on geosynchronous SAR: Theory, advantages and feasibility study

Due to the effects of the earth’s rotation and the satellite’s elliptical orbit, the Doppler centroid varies along the orbit in geosynchronous earth orbit synthetic aperture radar (GEO SAR). With an ultrahigh orbit height, the beam may illuminate outside the earth surface with a rotation angle large than 9 degrees. Therefore, the usual attitude steering methods to minimize the Doppler centroid in low earth orbit SAR (LEO SAR) may not be suitable for GEO SAR. In this paper, a novel steering strategy to maximize Nesigma0 for GEO SAR is proposed when the beam illuminates outside the earth surface. The new geometry and the gradient method are applied to demonstrate the method. The ability to stare imaging in geosynchronous orbit SAR is analyzed and verified by using the back projection (BP) algorithm to simulate the imaging principle. Nesigma0 under the new method improved 1dB comparing with the original image.

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.

Application of ionospheric tomography inversion to GPS data to study ionospheric electron-density distribution over China

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 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.

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.

<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>

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.

The computerized ionospheric tomography (CIT) has long been implemented in Global Navigation Satellite System systems. Recently, Phased Array L‐band Synthetic Aperture Radar (PALSAR) CIT has demonstrated unique advantages, particularly achieving spatial electron density reconstruction without ground‐based receivers. However, the inherent ill‐posedness of tomography limits PALSAR‐CIT reconstruction accuracy. To address this challenge, we calibrate the E‐CHAIM by adjusting the IG parameter under the constraint of GNSS‐TEC data. This integration yields a high‐precision initial background field for PALSAR‐CIT, effectively mitigating the ill‐posedness and significantly enhancing the overall reconstruction precision. Verifications show the improved E‐CHAIM (IE‐CHAIM) electron density profile matches ionosonde data better than alternative methods. Simultaneously, the proposed method based on the corrected initial value is validated using three PALSAR data sets near Poker Flat, Alaska. The data measured by the incoherent scatter radar (ISR) at the corresponding time nearby are regarded as true values. The results demonstrate that introducing the IE‐CHAIM as the initial background for PALSAR‐CIT significantly improves the reconstruction accuracy at ISR locations compared with using the E‐CHAIM, with decreases of 10.1%, 29.94%, and 26.65% in root‐mean‐square error (RMSE), respectively. Meanwhile, the Average Deviation (AD) values are decreased by 3.83%, 26.88%, and 35.74%, respectively. The results validate the significant role of the IE‐CHAIM driven by GNSS‐TEC in PALSAR‐CIT. By providing optimized initial constraints, the model enables PALSAR‐CIT to effectively improve the reconstruction accuracy of electron density profiles within the study area. Overall, the proposed IE‐CHAIM integration acts as a critical enabling layer that transforms spaceborne SAR data into reliable 3D ionospheric insights, especially in data‐sparse regions.

An L-band geosynchronous synthetic aperture radar (GEO SAR) will be inevitably affected by ionosphere scintillation because of its low carrier frequency. Meanwhile, compared with the low Earth orbit (LEO) SAR, a higher orbit of GEO SAR makes it have a longer integration time and a longer operation time within the susceptible regions of ionospheric scintillation. Thus, its imaging is more sensitive to ionospheric scintillation, and the corresponding degradation will have a different pattern. However, few works are focused on the quantitative analysis of the ionospheric scintillation impacts on L-band SAR. Moreover, the parameters of ionospheric irregularities utilized in the analyses are hard to be determined. In this paper, we first deduced the azimuth point-spread function with the consideration of both the amplitude and phase scintillation. Then, based on the measurable statistical parameters of ionospheric scintillation, performance specifications, including azimuth resolution, azimuth peak-to-sidelobe ratio (PSLR), and azimuth integrated sidelobe ratio (ISLR) are obtained to fully evaluate the impacts. The analysis suggests that in GEO SAR imaging, the azimuth ISLR severely deteriorates, whereas degradations of the azimuth resolution and PSLR are negligible. Finally, the simulations and a real ionospheric scintillation monitoring experiment by employing Global Positioning System satellites receivers were conducted, verifying the conclusions that the serious degraded contrast and focus quality of the images are brought by the raised azimuth ISLR.

Geosynchronous SAR tomography (GEO TomoSAR) techniques exploit multi‐pass SAR acquisitions of the same scene, taken with slightly different view angles, and allow generating fully 3D images. Compared to low earth orbit (LEO) SAR case, GEO SAR has the advantages of shorter revisit time period and wider coverage, which can greatly shorten the data acquisition time, improve the coherence of collected data and effectively improve the 3D imaging accuracy. However, GEO SAR has curved trajectories and un‐parallel repeated trajectories, which introduce the along‐track component of the spatial baseline. The obtained GEO TomoSAR data based on the data acquisition method using in LEO TomoSAR have the significant rotation‐induced decorrelation. Thus, the imaging performance in the elevation is severely degraded. In this paper, we first analyse the feasibility of GEO TomoSAR to achieve 3D imaging. In view of the special issues existing in GEO TomoSAR, we adopt an optimal minimal rotation‐induced decorrelation data acquisition method to obtain repeated trajectories data, which can effectively improve the coherence between the collected data. Then, performance of imaging in elevation was analysed based on the spatial baseline distribution. Finally, the feasibility and validity of GEO TomoSAR techniques are validated through computer simulation.

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.