High-Resolution ISAR Imaging by Exploiting Sparse Apertures

Zhang, Lei; Qiao, Zhijun; Xing, Mengdao; Sheng, Jian-Lian; Guo, Rui; Bao, Zheng

doi:10.1109/tap.2011.2173130

Cited by 174 publications

(96 citation statements)

References 33 publications

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“…As an optimal candidate for the SA-ISAR phase adjustment, the weighted eigenvector method performs well in adverse circumstances, such as highly noisy and SA cases. After motion compensation by the proposed autofocus approach, an SA-ISAR imaging method is applied to coherently focus SA-ISAR data for full-aperture-resolution image [3]. The imaging results of simulated and measured data validate the effectiveness of the proposed method.…”

Section: Introductionmentioning

confidence: 79%

“…To handle this, several methods are proposed, which can be divided into two major categories. The first category is the compressive sensing-based high-resolution SA-ISAR imaging methods [1][2][3][4][5][6]. These methods are able to achieve an exact or close recovery of signal by solving a minimum l 1 optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…In the 'Weighted eigenvector method for phase error correction' section, both the theory principle and operation flow of the weighted eigenvector algorithm are illustrated. Moreover, a simple description of the SA-ISAR imaging scheme [3] has been described. In the 'Performance analysis' section, autofocusing and imaging experiments are carried out with acquired ISAR data sets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

Duan

Zhang

Xing

2013

EURASIP J. Adv. Signal Process.

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With the development of multi-functional radar systems, inverse synthetic aperture radar (ISAR) imaging with sparse-aperture (SA) data has drawn considerable attention in the recent years. Motion compensation and imaging are among the most significant challenges that SA-ISAR imaging frequently faces. In this paper, we focus on the autofocus scheme, in which a modified eigenvector-based autofocus method is proposed. In the method, different weights are endued to different range cells according to their signal-to-noise ratios (SNRs). Using the weights, the contribution from the range cells with high SNR is enhanced, yielding accuracy improvement in phase error estimation. What is more is that to improve the estimation precision, an iterative scheme is introduced. Experimental results show that the proposal is not only robust to severe noise but also applicable to ISAR imaging with different SA patterns. Detailed comparisons are given in order to show the superiorities of the proposal in phase adjustment for ISAR data.

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Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

Duan

Zhang

Xing

2013

EURASIP J. Adv. Signal Process.

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“…For instance, the overwhelming amount of resource requests for storing and operating in receiver since the high sampling rate; in addition, the conventional imaging method, such as polar format algorithm (PFA), suffers from the sensibility to sidelobe disturbance, which leads to it being unsuitable for high-resolution imaging. 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].…”

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.

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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.

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“…As we know, the application of CS has two preconditions that: (1) the under reconstructed signal is sparse or compressible, (2) the measurements are incoherent. Since many SAR/ISAR images are sparse or compressible in some proper bases, the CS theory has shown very good prospects in SAR/ISAR applications [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A Novel Sparse Stepped Chaotic Signal and Its Compression Based on Compressive Sensing

Yang¹,

Zhang²

2013

PIER

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Abstract-We propose a novel signal model by combining the sparse stepped frequency signals with chaotic signals, i.e., the sparse stepped chaotic signal (SSCS) model, as well as the corresponding compression algorithm based on compressed sensing. In SSCS, the chaotic signals are modulated to sparse stepped frequencies to compose a transmitting burst. When receiving, the echo signals are demodulated to the baseband and then can be sampled directly at a rate much lower than the Nyquist rate determined by the bandwidth of chaotic signal of each subpulse. Compared with radars using conventional stepped frequency waveforms, the SSCS radar can transmit fewer subpulses in a burst and directly use lower speed ADC next to the receiver. Both simulated and real radar data are processed to demonstrate the effectiveness of the proposed SSCS as well as the compression algorithm by which high resolution range profiles are very well reconstructed.

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High-Resolution ISAR Imaging by Exploiting Sparse Apertures

Cited by 174 publications

References 33 publications

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

A weighted eigenvector autofocus method for sparse-aperture ISAR imaging

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

A Novel Sparse Stepped Chaotic Signal and Its Compression Based on Compressive Sensing

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