Sparse Reconstruction for Sar Imaging Based on Compressed Sensing

Wei, Shunjun; Zhang, Xiaoling; Shi, Jun; Gao, Xiang

doi:10.2528/pier10080805

Cited by 130 publications

(98 citation statements)

References 18 publications

(24 reference statements)

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“…. , N TC }, is arranged in descend order as, (16) where N P m [1] denotes the largest entry. The sudden change between N P m [k] and N P m [k + 1] is caused by the large difference between the number of points in the true track and the false track.…”

Section: The Proposed Cs-tbd Algorithmmentioning

confidence: 99%

“…A great deal of compressed sensing based methods have been applied to radar systems [8][9][10][11][12][13][14][15][16][17][18], which recover the target scene from fewer measurements than traditional methods. In [8], it is demonstrated that the compressed sensing can eliminate the need for matched filter at the receiver and has the potential to reduce the required sampling rate.…”

Section: Introductionmentioning

confidence: 99%

“…[10] focuses on monostatic chaotic multiple-input-multiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsity-exploiting algorithms for the parameter estimation of targets at Low-SNR. In the context of synthetic aperture radar (SAR), [11][12][13][14][15][16] present compressed sensing based data acquisition and imaging algorithms. An additional sensing matrix H is introduced in [17,18], which compress the received signal further by making nonadaptive, linear projections of the direct data sampled at the Nyquist frequency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Liu¹,

Han²,

Yao³

et al. 2013

PIER

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Abstract-This paper presents a novel compressed sensing based track before detect (CS-TBD) algorithm. The proposed algorithm reconstructs the whole radar scenario (direction of arrival (DOA)-Doppler plane) for each range gate at consecutive scans using an improved stagewise orthogonal matching pursuit (StOMP) algorithm, resulting in a three-dimensional range-DOA-Doppler space. It then performs temporal tracking in the newly built three-dimensional range-DOA-Doppler space, based on the information from multiple illuminations during each scan, as well as among consecutive scans. In the proposed CS-TBD algorithm, the improved StOMP algorithm together with the temporal tracking, can effectively distinguish true targets from false targets and clutter based on information from multiple illuminations.

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Section: The Proposed Cs-tbd Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Liu¹,

Han²,

Yao³

et al. 2013

PIER

View full text Add to dashboard Cite

show abstract

“…Recently, the theory of compressed sensing (CS) has attracted increased research interests to deal with the imaging problem when the data set is obtained not following Nyquist theory [12,13]. However, CS-based reconstruction still belongs to the scope of discrete parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Grid-Independent Imaging for Terahertz 2-D Synthetic Aperture Radar With Spatial Under-Sampling

Ding¹,

Ding²,

Ye³

et al. 2017

PIER

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Abstract-For the purpose of two-dimensional (2-D) imaging in the Terahertz (THz) near field through 2-D synthetic aperture radar technology, Fourier transform (FT) is one of the most popular imaging ways. However, FT-based algorithms would encounter performance loss either when spatial sampling is under Nyquist frequency or there are off-grid scatterers in the scene of interest. Therefore, by exploiting the theory of matrix enhancement and continuous parameter estimation, we propose to use matrix enhancement and matrix pencil (MEMP) method and matched filter to deal with arbitrarily located scatterers when spatial under-sampling is adopted. Through constructing a specifically expanded matrix, the information of the scatterers involved in the small data set can be enhanced. Then, highresolution grid-independence 2-D imaging can be achieved by the combination of MP and matched filter. Simulation results verify the effectiveness of the proposed algorithm.

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“…In [5], Baraniuk and Steeghs suggest to apply CS to radar imaging. After that, the CS based SAR imaging [6,7], ISAR imaging [8,9] and other applications of microwave imaging [10][11][12] are extensively discussed. A more detailed and comprehensive account can be found in [4,13,14].…”

Section: Introductionmentioning

confidence: 99%

Lasso Based Performance Evaluation for Sparse One-Dimensional Radar Problem Under Random Sub-Sampling and Gaussian Noise

Yin¹,

Zhang²,

Hong³

2013

PIER

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Abstract-Sparse microwave imaging is the combination of microwave imaging and sparse signal processing, which aims to extract physical and geometry information of sparse or transformed sparse scene from least number of radar measurements. As a primary investigation on its performance, this paper focuses on the performance guarantee for a one-dimensional radar, which detects delays of several point targets located at a sparse scene via randomly sub-sampling of radar returns. Based on the Lasso framework, the quantity relationship among three important factors is discussed, including the sub-sampling ratio ρ M , sparse ratio ρ K and signal-to-noise ratio (SNR), where ρ M is the ratio of number of random sub-sampling to that of Nyquist's sampling, and ρ K is the ratio of sparsity to the number of unknowns. In particular, to ensure correct delay detection and accurate back scattering coefficient reconstruction for each target, one needs ρ M to be greater than C(ρ K )ρ K log N and the input SNR be of order log N , where N is the number of range cells in scene.

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Sparse Reconstruction for Sar Imaging Based on Compressed Sensing

Cited by 130 publications

References 18 publications

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

Compressed Sensing Based Track Before Detect Algorithm for Airborne Radars

High-Resolution Grid-Independent Imaging for Terahertz 2-D Synthetic Aperture Radar With Spatial Under-Sampling

Lasso Based Performance Evaluation for Sparse One-Dimensional Radar Problem Under Random Sub-Sampling and Gaussian Noise

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