SAR imaging of multiple maritime moving targets based on sparsity Bayesian learning

Zhang, Yun; Mu, Huilin; Xiao, Tian; Jiang, Yicheng; Ding, Chuxiong

doi:10.1049/iet-rsn.2020.0160

Cited by 10 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process of the signal formation is regarded as a spatial transformation of the 3D image into a 2D SAR signal. Through 2D Taylor expansion of the projection operator's argument, i.e., the argument of the exponential term in Expression (8), the 3D coordinates of the target's scattering points are transformed into 2D coordinates in the image plane. Dimensionless cross-range and range coordinates of scattering points are analytically derived and physically interpreted.…”

Section: Discussionmentioning

confidence: 99%

“…Expression ( 8) is used to model a demodulated LFM SAR signal reflected by all scattering points from the entire 3D object's space. The distance R Sijk (p) and the image a ijk in (8) are defined in a three-dimensional space, whereas the synthetic aperture radar signal is described in a 2D signal plane (k, p). Hence, the multiplication of a ijk with an exponential term limited by rectangular function and summation on 3D discrete coordinates in Equation ( 8) performs a spatial transformation of the 3D object's image function a ijk into a 2D SAR signal function Ŝ(k, p).…”

Section: Analysis and Physical Interpretation Of A Sar Signal's Struc...mentioning

confidence: 99%

“…A focusing method for SAR ship target imaging applying an ISAR approach is presented in [7]. Based on the sparsity of ship targets in a SAR image, an image-reconstruction method for multiple ship targets is proposed in [8]. A regression approach combined with convolutional neural networks to estimate radial velocity and slant range components of moving objects is considered in [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

Lazarov

2022

Electronics

View full text Add to dashboard Cite

Maritime application of synthetic aperture radar (SAR) technology for sea-target surveillance and imaging is considered in this study. A SAR scenario, including the kinematics of a SAR satellite and a ship moving on the sea, along with the geometry of the target, are analytically described. A linear frequency modulation (LFM) waveform is applied for the target’s illumination. Based on the target’s geometry, SAR and target kinematics and the LFM waveform, a SAR signal model is synthesized. It is proven that the process of signal formation is a transformation of the three-dimensional (3D) image into a two-dimensional (2D) signal, whereas the target’s 2D imaging is an inverse transformation of the 2D signal into the target’s 2D image. SAR signal components, linear Fourier terms and higher-order phase terms are analytically derived and discussed in detail. Moreover, it is proven that SAR image reconstruction is a motion-compensation procedure, i.e., it removes all phases induced by first- and higher-order motion. Based on the SAR signal analysis, an illustrative iterative image-reconstruction algorithm is derived. The quality of the imaging is evaluated by an entropy cost function. Simulation experiments are carried out to verify the correctness of the theoretical statements in respect of SAR signal formation and image reconstruction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Analysis and Physical Interpretation Of A Sar Signal's Struc...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

Lazarov

2022

Electronics

View full text Add to dashboard Cite

show abstract

“…When a point target swings within a short period, it will produce a phase error with sinusoidal changes, causing the target to spread in the azimuth direction in the SAR image. The technique to finely focus ship target with oscillatory motions has been investigated for monostatic case in [45,46], and the multistatic GEO SAR case will also be considered in our future research. It can be observed that S rd r, f a + 4π λ k m1 , ϑ s still includes the term k m1 which is related to the multi-channel path difference.…”

Section: Discussionmentioning

confidence: 99%

Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR

et al. 2021

View full text Add to dashboard Cite

With the development trends of multistatic spaceborne synthetic aperture radar (SAR), geosynchronous SAR (GEO SAR) employing several formation-flying small satellites also has great potential for remote sensing. The small satellites can cooperate to acquire multi-channel data for moving target detection and parameter estimation in strong clutters. However, multistatic GEO SAR has large satellite spacing and a curved trajectory, which induce the near-field effects and channels out of alignment, respectively, bringing about challenges for the spatial adaptive processing. These problems produce a high-order term in the multi-channel slant range model, making the traditional model and adaptive processing method invalid. In this paper, to meet the requirement of SAR focusing, we firstly derive a fourth-order slant range model and a third-order path difference model for multistatic GEO SAR. Secondly, based on the derived model, the principle of stationary phase and series reversion method are utilized to derive the spatial steering vector for a moving target, which is a basis of spatial adaptive processing in the range-Doppler domain. Thirdly, the time-domain match filtering is constructed based on the fourth-order slant range model to image the moving target. Additionally, the moving targets are detected in the image domain. The motion parameter is estimated by iteratively maximizing the output signal to clutter and noise ratio (SCNR) through the range of possible target velocities. Finally, considering that the GEO SAR is still in development, the computer simulations are carried out to verify the effectiveness and evaluate the performance.

show abstract

“…In addition, sparse reconstruction algorithms often require iterations, and high computational complexity and time consumption. This greatly limits the use of sparse SAR imaging technology [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

Zhao

Liang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) imaging has developed rapidly in recent years. Although the traditional sparse optimization imaging algorithm has achieved effective results, its shortcomings are slow imaging speed, large number of parameters, and high computational complexity. To solve the above problems, an end-to-end SAR deep learning imaging algorithm is proposed. Based on the existing SAR sparse imaging algorithm, the SAR imaging model is first rewritten to the SAR complex signal form based on the real-value model. Second, instead of arranging the two-dimensional echo data into a vector to continuously construct an observation matrix, the algorithm only derives the neural network imaging model based on the iteration soft threshold algorithm (ISTA) sparse algorithm in the two-dimensional data domain, and then reconstructs the observation scene through the superposition and expansion of the multi-layer network. Finally, through the experiment of simulation data and measured data of the three targets, it is verified that our algorithm is superior to the traditional sparse algorithm in terms of imaging quality, imaging time, and the number of parameters.

show abstract

SAR imaging of multiple maritime moving targets based on sparsity Bayesian learning

Cited by 10 publications

References 28 publications

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

SAR Signal Formation and Image Reconstruction of a Moving Sea Target

Moving Target Detection and Parameter Estimation via a Modified Imaging STAP with a Large Baseline in Multistatic GEO SAR

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

Contact Info

Product

Resources

About