scholarly journals Feasibility of Replacing the Range Doppler Equation of Spaceborne Synthetic Aperture Radar Considering Atmospheric Propagation Delay with a Rational Polynomial Coefficient Model

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 553
Author(s):  
Shasha Hou ◽  
Yuancheng Huang ◽  
Guo Zhang ◽  
Ruishan Zhao ◽  
Peng Jia
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Polynomial Coefficient ◽  
Positioning Accuracy ◽  
Atmospheric Delay ◽  
Atmospheric Propagation ◽  
Rational Polynomial ◽  
Sar Imaging ◽  
Delay Correction ◽  
Aperture Radar

Usually, the rational polynomial coefficient (RPC) model of spaceborne synthetic aperture radar (SAR) is fitted by the original range Doppler (RD) model. However, the radar signal is affected by two-way atmospheric delay, which causes measurement error in the slant range term of the RD model. In this paper, two atmospheric delay correction methods are proposed for use in terrain-independent RPC fitting: single-scene SAR imaging with a unique atmospheric delay correction parameter (plan 1) and single-scene SAR imaging with spatially varying atmospheric delay correction parameters (plan 2). The feasibility of the two methods was verified by conducting fitting experiments and geometric positioning accuracy verification of the RPC model. The experiments for the GF-3 satellite were performed by using global meteorological data, a global digital elevation model, and ground control data from several regions in China. The experimental results show that it is feasible to use plan 1 or plan 2 to correct the atmospheric delay error, no matter whether in plain, mountainous, or plateau areas. Moreover, the geometric positioning accuracy of the RPC model after correcting the atmospheric delay was improved to better than 3 m. This is of great significance for the efficient and high-precision geometric processing of spaceborne SAR images.

scholarly journals A method for automated snow avalanche debris detection through use of synthetic aperture radar (SAR) imaging

2016 ◽  
Vol 3 (11) ◽  
pp. 446-462 ◽  
Author(s):  
H. Vickers ◽  
M. Eckerstorfer ◽  
E. Malnes ◽  
Y. Larsen ◽  
H. Hindberg
Keyword(s):  
Synthetic Aperture Radar ◽  
Snow Avalanche ◽  
Synthetic Aperture ◽  
Sar Imaging ◽  
Aperture Radar

Synthetic aperture radar (SAR) imaging based on low-cost FMCW radar for mini-UAVs

2016 ◽  
Author(s):  
Yarleque Medina ◽  
Manuel Augusto ◽  
Alvarez Navarro ◽  
Sthefany Martinez Odiaga ◽  
Hansel Joussef ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Low Cost ◽  
Synthetic Aperture ◽  
Fmcw Radar ◽  
Sar Imaging ◽  
Aperture Radar

scholarly journals Focusing Bistatic Forward-looking Synthetic Aperture Radar Based on An Improved Hyperbolic Range Model and A Modified Omega-K algorithm

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3792
Author(s):  
Chenchen Wang ◽  
Weimin Su ◽  
Hong Gu ◽  
Jianchao Yang
Keyword(s):  
Synthetic Aperture Radar ◽  
High Order ◽  
Synthetic Aperture ◽  
Approximation Procedure ◽  
Square Root ◽  
Slant Range ◽  
Receiver System ◽  
Sar Imaging ◽  
Aperture Radar ◽  
Forward Looking

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm.

scholarly journals Developing new multi-image InSAR methods for atmospheric delay estimation

2017 ◽  
Author(s):  
Αρλίντα Σακελλάρη
Keyword(s):  
Synthetic Aperture Radar ◽  
Digital Elevation Model ◽  
Delay Estimation ◽  
Synthetic Aperture ◽  
Atmospheric Delay ◽  
Digital Elevation ◽  
Elevation Model ◽  
Aperture Radar

Η διατριβή έχει ως στόχο να καθορίσει τίς βέλτιστες μεθόδους επεξεργασίας των απεικονίσεων Ραντάρ Συνθετικού Ανοίγματος (Synthetic Aperture Radar) (SAR) με σκοπό να παράγονται κατά το δυνατόν αξιόπιστα συμβολομετρικά προϊόντα. Η βέλτιστη επεξεργασία περιλαμβάνει τόσο βελτίωση της ποιότητας των απεικονίσεων SAR καθώς και των ενδιάμεσων προϊόντων τα οποία παράγονται κατά την συμβολομετρική διαδιακασία, όσο και την ανάπτυξη μεθοδολογιών για την ακριβή εκτίμηση όλων των συνιστωσών της συμβολομετρικής φάσης, θεωρώντας και την καθυστέρηση λόγω ατμόσφαιρας ως μια συνιστώσα της συμβολομετρικής φάσης. Ιδιαίτερη έμφαση έχει δοθεί στην εκτίμηση αυτής της συνιστώσας και ιδιαίτερα των επιδράσεων της τροπόσφαιρας, θεωρώντας ότι η συνιστώσα της μετατόπισης παρουσιάζεται σε περιορισμένο αριθμό εφαρμογών σε σχέση με αυτήν της ατμόσφαιρας η οποία είναι μία από τις κύριες αιτίες σφάλματος κατά την εκτίμηση του υψομέτρου με συμβολομετρία και διαφορική συμβολομετρία. Γι αυτό το λόγο, καθώς και για το γεγονός ότι οι μέθοδοι που έχουν αναπτυχθεί δεν βασίζονται στην αφαίρεση γνωστού Ψηφιακού Μοντέλου Εδάφους (Digital Elevation model) (DEM), οι μέθοδοι αυτές περιγράφονται καλύτερα από τον όρο «μέθοδοι Συμβολομετρίας πολλαπλών απεικονίσεων SAR» σε σχέση με τον ήδη υπάρχοντα όρο της διαφορικής Συμβολομετρίας.

scholarly journals Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

2021 ◽  
Vol 13 (22) ◽  
pp. 4670
Author(s):  
Fangjia Dou ◽  
Xiaolei Lv ◽  
Huiming Chai
Keyword(s):  
Synthetic Aperture Radar ◽  
Numerical Weather Prediction ◽  
Deformation Rate ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Synthetic Aperture ◽  
Tropospheric Delay ◽  
Numerical Weather ◽  
Atmospheric Delay ◽  
Aperture Radar

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

COMPRESSED SYNTHETIC APERTURE RADAR IMAGING BASED ON MAXWELL EQUATION

2016 ◽  
Vol 78 (6-3) ◽  
Author(s):  
Rahmat Arief ◽  
Dodi Sudiana ◽  
Kalamullah Ramli
Keyword(s):  
Synthetic Aperture Radar ◽  
Imaging System ◽  
Sampling Rate ◽  
Large Data ◽  
Synthetic Aperture ◽  
Fast Time ◽  
Match Filter ◽  
Compressed Sampling ◽  
Sar Imaging ◽  
Aperture Radar

Within few years backward, researches had presented the ability of compressive sensing to handle the large data problem on high resolution synthetic aperture radar (SAR) imaging. The main issue on CS framework that should be dealt with the SAR imaging is on the requirement of linearization on the measurement system. This paper proposes a new approach on formulating the compressed SAR echo imaging system which is derived from the Maxwell’s equations with continuous signal along the SAR antenna movement. Born approximation is applied to approximate the linear form of the SAR echo imaging system. In addition, the compressed sampling is formed by reducing the sampling rate of received radar signals randomly simultaneously on both of low sampling of fast time and slow time signals and by reducing the pulse period interval of transmitted signals. The simulation’s result shows that a better focused reconstructed sparse target can be achieved compared with the conventional match filter based Range Doppler (RD) method.

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