A Novel Strategy for Radar Imaging Based on Compressive Sensing

Alonso, Marivi Tello; López-Dekker, Paco; Mallorquí, J.J.

doi:10.1109/tgrs.2010.2051231

Cited by 268 publications

(86 citation statements)

References 21 publications

(2 reference statements)

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“…It is easy to show from Algorithm 1 that the computational complexity of the proposed method is O (L × M N × N M axiter ). For comparison, the computational complexities for the BP used in most of sparse representation based ISAR [2][8] and SAR [23][24] imaging methods, the BCS used in [6] and the proposed method are summarized in Table I. As demonstrated later, the proposed method converges quickly, N M axiter used in our method is much smaller than L, i.e., N M axiter L. Therefore, the computational complexity of the proposed algorithm can be written as O (L × M N ), and is smaller than that of the BP.…”

Section: B Discussion On the Proposed Imaging Algorithmmentioning

confidence: 99%

“…By exploiting the sparsity of ISAR/SAR images, sparsity based imagery methods are shown to outperform the conventional rangeDoppler algorithm (RDA) [16] in terms of achieving high resolution with fewer measurements. The l 1 -norm based BP is used to obtain a high-resolution image with fewer pulses in ISAR [2][8] and in SAR [23]. Apart from the l 1 -norm regularization, additional constraints and procedures are added for other purposes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene

Wang

Zhao

et al. 2014

IEEE Trans. Geosci. Remote Sensing

119

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Section: B Discussion On the Proposed Imaging Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene

Wang

Zhao

et al. 2014

IEEE Trans. Geosci. Remote Sensing

119

View full text Add to dashboard Cite

“…So, the sparsity of echo signal has been discussed for some special applications, such as ocean remote sensing. For the other condition, the previous literature [23,24] presented one kind of sensing matrices in time domain as follows. This CS matrix is based on the idea that the echo signal is the delay of the transmitted signal, and its orthogonality also was validated.…”

Section: Compressive Sensing Matrix For Sar Imagingmentioning

confidence: 99%

Compressive sensing imaging for general synthetic aperture radar echo model based on Maxwell’s equations

Sun

Cao

Chen

et al. 2014

EURASIP J. Adv. Signal Process.

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A general echo model is derived for the synthetic aperture radar (SAR) imaging with high resolution based on the scalar form of Maxwell's equations. After analyzing the relationship between the general echo model in frequency domain and the existing model in time domain, a compressive sensing (CS) matrix is constructed from random partial Fourier matrices for processing the range CS SAR imaging. Simulations validate the orthogonality of the proposed CS matrix and the feasibility of CS SAR imaging based on the general echo model.

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“…Although CS has enabled significant improvements on radar and radar imaging systems [2,[10][11][12][13], a number of challenges still exist in applying CS to radar imaging, such as developing appropriate sparsity models of radar images and managing computational complexity [5,6,14].…”

Section: Relation To Prior Workmentioning

confidence: 99%

Random steerable arrays for synthetic aperture imaging

Liu

Boufounos

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In classical spotlight-mode synthetic aperture radar (SAR), a mobile sensor array is steered to always focus on the same area (spot) as it moves, transmitting pulses to illuminate the spot and receiving and processing their reflections. The result is a high resolution image, covering a relatively small area. In this paper, we propose using a randomly steerable sensor array for synthetic aperture imaging, aiming to increase the coverage area without sacrificing the imaging resolution. This is realized by steering the beam of the array such that each transmitted pulse illuminates one of two or more spots, randomly selected with equal probability. Each of those spots has the same size as a single spot in a classical array, effectively doubling (or more) the total area illuminated. Using principles from compressive sensing (CS) we demonstrate that it is possible to reconstruct the images of all illuminated areas by exploiting the structure of the reconstructed images. Our experimental results demonstrate that our random steerable array can double coverage with almost the same imaging resolution. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTIn classical spotlight-mode synthetic aperture radar (SAR), a mobile sensor array is steered to always focus on the same area (spot) as it moves, transmitting pulses to illuminate the spot and receiving and processing their reflections. The result is a high resolution image, covering a relatively small area. In this paper, we propose using a randomly steerable sensor array for synthetic aperture imaging, aiming to increase the coverage area without sacrificing the imaging resolution. This is realized by steering the beam of the array such that each transmitted pulse illuminates one of two or more spots, randomly selected with equal probability. Each of those spots has the same size as a single spot in a classical array, effectively doubling (or more) the total area illuminated. Using principles from compressive sensing (CS) we demonstrate that it is possible to reconstruct the images of all illuminated areas by exploiting the structure of the reconstructed images. Our experimental results demonstrate that our random steerable array can double coverage with almost the same imaging resolution.

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A Novel Strategy for Radar Imaging Based on Compressive Sensing

Cited by 268 publications

References 21 publications

Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene

Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene

Compressive sensing imaging for general synthetic aperture radar echo model based on Maxwell’s equations

Random steerable arrays for synthetic aperture imaging

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