Squint Mode GEO SAR Imaging Using Bulk Range Walk Correction on Received Signals

Geng, Jiwen; Yu, Ze; Li, Chunsheng; Liu, Wei

doi:10.3390/rs11010017

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Operator Update Module (OUM): In this module, the main function is to implement Equation (21), where ρ i is the output of the Adaptive Unfolding Module, and then the output of the OUM at the layer i of the network is defined as:…”

Section: Network Structurementioning

confidence: 99%

“…In recent years, the application of compress sensing (CS) theory to SAR imaging has been rapidly developed [18][19][20][21][22]. In general, the number of moving targets in a SAR observation scene is finite, and thus the GMT echo signal satisfies sparsity, which allows the use of CS theory for imaging or parameter estimation of the compressed echo data.…”

Section: Introductionmentioning

confidence: 99%

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Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

Chen

Luo

et al. 2022

Remote Sensing

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Ground moving targets (GMT), due to the existence of velocity in range and azimuth direction, will lead to the deviation from their true position and defocus in the azimuth direction during the synthetic aperture radar (SAR) imaging process. To address this problem and compress the amount of echo data, a sparse SAR imaging method for ground moving targets is proposed. Specifically, we first constructed a two-dimensional sparse observation model of the GMT based on matched filter operators. Then, the observation model was solved by a deep network, GMT sparse imaging network (GMTSI-Net), which was mainly obtained by unfolding an iterative soft threshold algorithm (ISTA)-based iterative solution. Furthermore, we designed an adaptive unfolding module in the imaging network to improve the adaptability of the network to the input of echo data with different sampling ratios. The proposed imaging network can achieve faster and more accurate SAR images of ground moving targets under a low sampling ratio and signal-to-noise ratio (SNR). Simulated and measured data experiments were conducted to demonstrate the performance of imaging quality of the proposed method.

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Section: Network Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

Chen

Luo

et al. 2022

Remote Sensing

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“…0 R denotes the slant range at i  . Each coefficient j r depends on ephemeris data, geographic information of the target zone [12][13] and the atmospheric turbulence [14][15][16]. The detailed description is given in Appendix I.…”

Section: A Slant Rangementioning

confidence: 99%

“…According to the atmospheric refractivity profile (ARP) data provided by the Chinese meteorological satellite FengYun-3 (FY-3) in [16], the second and third order derivative of tropospheric range error ( 2 q and 3 q ) are 7 5.03 10    m/s 2 and 9 4.89 10   m/s 3 , respectively [16]. [12] indicates that it is sufficient to image the stationary target in GEO SAR by setting the order of the range history as 5. The order of the range history of moving target (with translational motion) in GEO SAR is set as 3 [28].…”

Section: Appendix Imentioning

confidence: 99%

Signal Separation in GEO SAR Imaging of Maneuvering Ships by Removing Micro-Motion Effect

Guo

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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P is totally removed and the blue curve becomes flat. However, due to mismatching, the signal of B P is still modulated by the micro-motion and the red curve in Fig. 3(c) fluctuates. If Fourier transform is directly performed on A P in the azimuth direction after micro-motion compensation, the imaging result of A P will be degraded because of the interference caused by micro-motion of B P , as shown in Fig. 3(d). Therefore, signal separation must be performed by removing the micro-motion effect to guarantee the imaging quality. III. BASIC IDEA OF SIGNAL SEPARATIONSince the time-frequency characteristics of various scatterers are different, imaging needs to be performed scatterer by scatterer.

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“…On the other hand, because the orbit altitude of GEO SAR is about 36,000 km, a more accurate slant range model is necessary [16][17][18], and the second-order slant range model in geosynchronous orbit is no longer applicable. At present, some people have proposed the high-order Taylor expansion slant range model in the GEO SAR imaging algorithm [19][20][21], but it is only for the imaging of point targets and does not consider the elevation space variation. Or, the simple classical second-order slant range model is still adopted in some papers considering the elevation space variation [10][11][12], which will introduce a large phase error in GEO SAR.…”

Section: Introductionmentioning

confidence: 99%

Elevation Spatial Variation Analysis and Compensation in GEO SAR Imaging

Chang

Dong

et al. 2021

Remote Sensing

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Due to geosynchronous synthetic aperture radar’s (GEO SAR) high orbit and low relative speed, the integration time reaches up to hundreds of seconds for a fine resolution. The short revisit cycle is essential for remote sensing applications such as disaster monitoring and vegetation measurements. Three-dimensional (3D) scene imaging mode is crucial for long-term observation using GEO SAR. However, this mode will bring a new kind of space-variant error in elevation. In this paper, we focus on the analysis of the elevation space-variant error. First, the decorrelation problems caused by the spatial variation are presented. Second, by combining with the SAR imaging geometry, the elevation spatial variation is decomposed into two-dimensional (2D) space variation of range and azimuth. Third, an imaging algorithm is proposed to solve the 3D space variation and improve the focusing depth. Finally, simulations with dot-matrix targets and distributed targets are performed to validate the imaging method.

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Squint Mode GEO SAR Imaging Using Bulk Range Walk Correction on Received Signals

Cited by 7 publications

References 20 publications

Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

Sparse SAR Imaging Method for Ground Moving Target via GMTSI-Net

Signal Separation in GEO SAR Imaging of Maneuvering Ships by Removing Micro-Motion Effect

Elevation Spatial Variation Analysis and Compensation in GEO SAR Imaging

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