A Compensation Method for Airborne SAR with Varying Accelerated Motion Error

Yi, Tianzhu; He, Zhihua; He, Feng; Dong, Zhen; Wu, Meiping; Song, Yongping

doi:10.3390/rs10071124

Cited by 11 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Denote X ∈ C N a ×N r as a defocused SAR image, where N a , N r are the number of pixels in azimuth and range, respectively. We can obtain M estimated phase errror vectors φ (1) , φ (2) , • • • , φ (M) . These vectors are used to compensate for the defocused image X, and M focused images Y (1) , Y (2) , • • • , Y (M) are obtained.…”

Section: Ensemble Schemementioning

confidence: 99%

“…We can obtain M estimated phase errror vectors φ (1) , φ (2) , • • • , φ (M) . These vectors are used to compensate for the defocused image X, and M focused images Y (1) , Y (2) , • • • , Y (M) are obtained. Finally, our proposed metric-based combination strategy is applied to these images to obtain the final result.…”

Section: Ensemble Schemementioning

confidence: 99%

“…However, the imaging quality of SAR is usually degraded by undesired Phase Errors (PEs). These PEs usually come from trajectory deviations and the instability of the platform velocity [2]. The uncompensated PEs will cause serious image blurring and geometric distortion of the SAR imagery [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

Inaccurate Synthetic Aperture Radar (SAR) navigation information will lead to unknown phase errors in SAR data. Uncompensated phase errors can blur the SAR images. Autofocus is a technique that can automatically estimate phase errors from data. However, existing autofocus algorithms either have poor focusing quality or a slow focusing speed. In this paper, an ensemble learning-based autofocus method is proposed. Convolutional Extreme Learning Machine (CELM) is constructed and utilized to estimate the phase error. However, the performance of a single CELM is poor. To overcome this, a novel, metric-based combination strategy is proposed, combining multiple CELMs to further improve the estimation accuracy. The proposed model is trained with the classical bagging-based ensemble learning method. The training and testing process is non-iterative and fast. Experimental results conducted on real SAR data show that the proposed method has a good trade-off between focusing quality and speed.

show abstract

Section: Ensemble Schemementioning

confidence: 99%

Section: Ensemble Schemementioning

confidence: 99%

See 1 more Smart Citation

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Dividing the echo data into multiple subapertures was originally for applying PGA to strip mode [20]. The process of multi-subaperture autofocus is usually getting the SPE gradients from the subaperture in azimuth time domain first, then combining them to get the error gradient function of the whole aperture, next adding up to get the phase error function, finally compensating the whole data in the azimuth-time domain [11].…”

Section: The Multi-subaperture Autofocus Algorithmsmentioning

confidence: 99%

Improvement of Stripmap SAR Autofocus Based on Minimum-Entropy Criterion

T¹,

Fu²,

Zhang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.

show abstract

“…It has high azimuth resolutions due to relative movement between antenna and target. However, this platform movement also poses difficulties for accurate imaging [6], and hence motion compensation (MOCO) [7][8][9] is an essential procedure for SAR imaging. Furthermore, for unmanned aerial vehicle (UAV) SAR systems [10][11][12] working at a low altitude, their flight trajectory is usually disturbed by severe atmospheric turbulence because of the small size and light weight of UAV platform [13], which inevitably causes serious blurring and geometric distortion in SAR images.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

A Compensation Method for Airborne SAR with Varying Accelerated Motion Error

Cited by 11 publications

References 11 publications

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

Fast SAR Autofocus Based on Ensemble Convolutional Extreme Learning Machine

Improvement of Stripmap SAR Autofocus Based on Minimum-Entropy Criterion

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

Contact Info

Product

Resources

About