Resolution enhancement for ISAR imaging via improved statistical compressive sensing

Zhang, Lei; Wang, Hongxian; Qiao, Zhijun

doi:10.1186/s13634-016-0379-2

Cited by 28 publications

(23 citation statements)

References 58 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assuming that σ obeys the Laplacian prior distribution [26], the probability distribution function (PDF) of σ is…”

Section: B-isar Imaging Methods Of High-speed Target Based On Sparse Imentioning

confidence: 99%

“…Then, known from the first Sinc function of equation (26) and ϕ 0 in equation (27), the calibration relationship between the ordinate y of the scattering point and the frequency f i of the fast time is…”

Section: B-isar Range-doppler Imaging Analysis Ofmentioning

confidence: 99%

“…From the second Sinc function of equation (26), the calibration relationship between the ordinates x, y of the scattering point and the rotation Doppler f d is…”

Section: B-isar Range-doppler Imaging Analysis Ofmentioning

confidence: 99%

See 2 more Smart Citations

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Zeng

Zhu

Xin

2019

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

Bistatic inverse synthetic aperture radar (ISAR) can increase the probability of tracking the high-speed target and provide more angle information than monostatic ISAR. However, bistatic ISAR suffers from a serious defocusing problem, resulting from the high-speed motion. Furthermore, the inherent geometry distortion and calibration problems make bistatic ISAR (B-ISAR) imaging more complex. In response to these problems, we propose a bistatic ISAR imaging method for high-speed moving target with geometric distortion correction and calibration based on dechirping processing. At first, based on the motion decomposition idea, the B-ISAR echo model of the high-speed moving target is established. Then, by analyzing the imaging mechanism of the Range-Doppler algorithm (RDA), we eliminate the phase items influencing the imaging quality with speed compensation and Doppler compensation. After that, the analytic formula of the geometric distortion factor and calibration factor are deduced, which helps transform the geometric correction and calibration problem into a parameter estimation problem. Finally, with the sparsity of the scattering points, the required parameters are solved using the expectation maximization (EM) algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed moving target with geometric correction and calibration is obtained. The simulations show that the proposed method has a better resolution and simultaneously attains geometric correction and calibration of the image.

show abstract

“…Assuming that σ obeys the Laplacian prior distribution [26], the probability distribution function (PDF) of σ is…”

Section: B-isar Imaging Methods Of High-speed Target Based On Sparse Imentioning

confidence: 99%

Section: B-isar Range-doppler Imaging Analysis Ofmentioning

confidence: 99%

See 1 more Smart Citation

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Zeng

Zhu

Xin

2019

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…Because of its distribution with peak and thick tail, it is a more suitable distribution to describe the financial data than the normal distribution, so it has more research and application in the field of finance [1][2][3][4][5][6][7]. In recent years, the distribution has also been applied to image analysis, mechanical engineering and other fields [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function

Li¹

2017

IJDSA

View full text Add to dashboard Cite

Loss function is one of the most topics in Bayesian analysis. The aim of this paper is to study the estimation of the shape parameter of Laplace distribution using Bayesian technique under a new loss function, which is a compound function of LINEX function. The Bayes estimator of the parameter is derived under the prior distribution of the parameter based on Gamma prior distribution. Furthermore, Monte Carlo statistical simulations illustrate that the Bayes estimators obtained under LINEX-based loss function is affected by the prior parameter and the value of the shape parameter of the LINEX-based loss function. But when the sample size is large, they have less influence on the estimation result.

show abstract

“…For solving the problem of high demand on hardware, the compressed sensing (CS) framework has been introduced into SAR imaging [7][8][9][10][11], which can extract necessary information at a lower sampling rate than Nyquist limit [12][13][14]. Furthermore, the CS-based approach is able to accomplish SAR imaging with the low sidelobe and possess the robustness to noise [15][16][17][18][19][20]. But the existing methods only utilize the spatial sparsity of the observation without taking advantage of structural features of target scene.…”

Section: Introductionmentioning

confidence: 99%

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

Gao

Gui

Cong

et al. 2017

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

show abstract

Resolution enhancement for ISAR imaging via improved statistical compressive sensing

Cited by 28 publications

References 58 publications

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Bayes Estimation of Parameter of Laplace Distribution Under a New LINEX-Based Loss Function

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

Contact Info

Product

Resources

About