scholarly journals A Spatial Variant Motion Compensation Algorithm for High-Monofrequency Motion Error in Mini-UAV-Based BiSAR Systems

2021 ◽  
Vol 13 (17) ◽  
pp. 3544
Author(s):  
Zhanze Wang ◽  
Feifeng Liu ◽  
Simin He ◽  
Zhixiang Xu
Keyword(s):  
Motion Compensation ◽  
High Frequency ◽  
Error Model ◽  
Joint Estimation ◽  
Synthetic Aperture ◽  
Spatial Variance ◽  
Motion Error ◽  
Compensation Algorithm ◽  
Aerial Vehicle ◽  
Bistatic Synthetic Aperture Radar

High-frequency motion errors can drastically decrease the image quality in mini-unmanned-aerial-vehicle (UAV)-based bistatic synthetic aperture radar (BiSAR), where the spatial variance is much more complex than that in monoSAR. High-monofrequency motion error is a special BiSAR case in which the different motion errors from transmitters and receivers lead to the formation of monofrequency motion error. Furthermore, neither of the classic processors, BiSAR and monoSAR, can compensate for the coupled high-monofrequency motion errors. In this paper, a spatial variant motion compensation algorithm for high-monofrequency motion errors is proposed. First, the bistatic rotation error model that causes high-monofrequency motion error is re-established to account for the bistatic spatial variance of image formation. Second, the corresponding parameters of error model nonlinear gradient are obtained by the joint estimation of subimages. Third, the bistatic spatial variance can be adaptively compensated for based on the error of the nonlinear gradient through contour projection. It is suggested based on the simulation and experimental results that the proposed algorithm can effectively compensate for high-monofrequency motion error in mini-UAV-based BiSAR system conditions.

scholarly journals Effects of Motion Compensation Residual Error and Polarization Distortion on UAV-Borne PolInSAR

2021 ◽  
Vol 13 (4) ◽  
pp. 618
Author(s):  
Zexin Lv ◽  
Fangfang Li ◽  
Xiaolan Qiu ◽  
Chibiao Ding
Keyword(s):  
Motion Compensation ◽  
Residual Error ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Practical Application ◽  
Aerial Vehicle ◽  
Application Potential ◽  
Large Motion ◽  
Simulation Results ◽  
Interferometric Sar

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) can improve interferometric coherence and phase quality, which has good application potential. With the development of the Mini-SAR system, Unmanned Aerial Vehicle borne (UAV-borne) PolInSAR systems became a reality. However, UAV-borne PolInSAR is easily affected by air currents and other factors, which may cause large motion errors and polarization distortion inevitably exists. However, there are few pieces of research which are about motion compensation residual error (MCRE) and polarization distortion effects on PolInSAR. Though the effects of MCRE on Interferometric SAR (InSAR) and polarization distortion on PolInSAR were studied, respectively, these two parts are independently modeled and analyzed. In this paper, a model that simultaneously considers the effects of these two kinds of errors is proposed, and simulation results are given to validate the model. Then, a quantitative analysis based on a real Quadcopter UAV PolInSAR system is performed according to the model, which is valuable for system design and practical application of the UAV-borne PolInSAR system.

scholarly journals Residual Motion Error Correction with Backprojection Multisquint Algorithm for Airborne Synthetic Aperture Radar Interferometry

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2342 ◽  
Author(s):  
Pengfei Xie ◽  
Man Zhang ◽  
Lei Zhang ◽  
Guanyong Wang
Keyword(s):  
Synthetic Aperture Radar ◽  
Error Correction ◽  
Motion Compensation ◽  
Mathematical Expression ◽  
Real Data ◽  
Synthetic Aperture ◽  
Motion Error ◽  
Digital Elevation ◽  
Residual Motion ◽  
Aperture Radar

For airborne interferometric synthetic aperture radar (InSAR) data processing, it is essential to achieve precise motion compensation to obtain high-quality digital elevation models (DEMs). In this paper, a novel InSAR motion compensation method is developed, which combines the backprojection (BP) focusing and the multisquint (MSQ) technique. The algorithm is two-fold. For SAR image focusing, BP algorithm is applied to fully use the navigation information. Additionally, an explicit mathematical expression of residual motion error (RME) in the BP image is derived, which paves a way to integrating the MSQ algorithm in the azimuth spatial wavenumber domain for a refined RME correction. It is revealed that the proposed backprojection multisquint (BP-MSQ) algorithm exploits the motion error correction advantages of BP and MSQ simultaneously, which leads to significant improvements of InSAR image quality. Simulation and real data experiments are employed to illustrate the effectiveness of the proposed algorithm.

scholarly journals SAR Mocomp by machine Learning

2021 ◽  
Author(s):  
Brianna Christensen ◽  
Enson Chang ◽  
Nathaniel Tamminga
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Unmanned Aerial Vehicles ◽  
Motion Compensation ◽  
Synthetic Aperture ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Motion Error ◽  
Aerial Vehicles ◽  
Sudden Motion

All unmanned aerial vehicles that use synthetic aperture radar (SAR) systems are equipped with inertial navigation systems (INS) to reduce motion error. Additional motion compensation (MOCOMP) from the data itself is still necessary to achieve required accuracy of a SAR. An affordable method for small drones has yet to be created. We propose machine learning with deep convolutional neural network (CNN) to extract motion error such as sway (right and left) and surge (forward). Results show that the CNN is capable of recognizing gradual drone motion deviations. It has the potential to pick up sudden motion error as well, overcoming major deficiencies of traditional MOCOMP methods, and the need for INS.

scholarly journals Robust Two-Dimensional Spatial-Variant Map-Drift Algorithm for UAV SAR Autofocusing

2019 ◽  
Vol 11 (3) ◽  
pp. 340 ◽  
Author(s):  
Guanyong Wang ◽  
Man Zhang ◽  
Yan Huang ◽  
Lei Zhang ◽  
Fengfei Wang
Keyword(s):  
Cross Correlation ◽  
Phase Error ◽  
Synthetic Aperture ◽  
Two Dimensional ◽  
Spatial Variance ◽  
Phase Errors ◽  
Residual Phase ◽  
Aerial Vehicle ◽  
Sar Imagery ◽  
The One

Autofocus has attracted wide attention for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) systems, because autofocus process is crucial and difficult when the phase error is spatially dependent on both range and azimuth directions. In this paper, a novel two-dimensional spatial-variant map-drift algorithm (2D-SVMDA) is developed to provide robust autofocusing performance for UAV SAR imagery. This proposed algorithm combines two enhanced map-drift kernels. On the one hand, based on the azimuth-dependent phase correction, a novel azimuth-variant map-drift algorithm (AVMDA) is established to model the residual phase error as a linear function in the azimuth direction. Then the model coefficients are efficiently estimated by a quadratic Newton optimization with modified maximum cross-correlation. On the other hand, by concatenating the existing range-dependent map-drift algorithm (RDMDA) and the proposed AVMDA in this paper, a phase autofocus procedure of 2D-SVMDA is finally established. The proposed 2D-SVMDA can handle spatial-variance problems induced by strong phase errors. Simulated and real measured data are employed to demonstrate that the proposed algorithm compensates both the range- and azimuth-variant phase errors effectively.

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