High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition

Wong, S.K.

doi:10.1109/taes.2009.5259180

Cited by 37 publications

(20 citation statements)

References 17 publications

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“…T -F matrices NNSC feature extraction was obtained for 10,20,30,40,50,60,70,80,90,100,120,150 and 200 basis components as described in Section 3. This was done to analyze how the classification results are affected by the number of learning basis.…”

Section: Learning Basismentioning

confidence: 99%

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HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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Abstract-A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) profiles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR profiles. And the non-negative time-frequency matrix for each of the profiles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classification decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the effectiveness of the proposed technique for SAR target classification.

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Section: Learning Basismentioning

confidence: 99%

“…Physics-based features can be extracted using scattering center models, which provide a concise, physically relevant description of the target [9]. There are also several target features extraction methods based on shadow regions in a SAR image [10].…”

Section: Introductionmentioning

confidence: 99%

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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show abstract

“…Moving target detection, imaging, and velocity estimation are active research fields with civilian and military applications in synthetic aperture radar (SAR) community [1][2][3][4][5][6][7][8][9][10][11][12][13]. Many applications aim at determining the position and the velocity of certain targets.…”

Section: Introductionmentioning

confidence: 99%

Moving-Target Velocity Estimation in a Complex-Valued Sar Imagery

Li¹,

Lv²,

Liu³

2013

PIER

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Abstract-In the ground moving target indication with synthetic aperture radar (SAR) community, algorithms used to estimate the velocity of a detected moving target are important because they are relative to the topics about refocusing and azimuth displacement correction. The velocity is regarded as a vector with two components, one in azimuth and one in range direction, and new algorithms aiming at estimating the two components are proposed and verified. The range velocity estimator transforms a detected patch containing a moving target to range Doppler domain by using the 1-D fast Fourier Transform in each range bin to achieve its range Doppler locus. The slope of the range Doppler locus is computed by using the Radon Transform on the range Doppler plane and the range velocity component is worked out according to radar system parameters and the slope value. Two estimators are proposed to compute the azimuth velocity component. One is based on symmetric defocusing in Doppler domain, the other is based on phase gradient in wave-number domain. Experiments confirm the effectiveness of the estimators by using simulated and field data.

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“…In practice, the system is so complex that both high hardware and computation efforts are needed in implementation [3][4][5][6][7][8][9][10]. Recently many GMTI methodologies based on a single antenna SAR or a single complex-valued SAR image, e.g., auto-focusing [11], antenna beam patten transforming [12], and SAR stacks [13], were developed and got many effective results.…”

Section: Introductionmentioning

confidence: 99%

Ground moving target indication and parameters estimation using a dual-frequency synthetic aperture radar

Wang

Liu

et al. 2012

EURASIP J. Adv. Signal Process.

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A new scheme is presented to estimate the range and azimuth velocity components of a detected moving target by using a dual-frequency synthetic aperture radar (SAR). It consists of a moving target detector, a range velocity estimator, and an azimuth velocity estimator. In this scheme, two original SAR images are achieved from the returns first, and then processed by a symmetric defocusing filter pair (SDFP) to produce two defocused images. By comparing the sharpness of the two defocused images, the moving targets are indicated and isolated form each original SAR image. For a selected moving target, its range velocity component is estimated by using a Doppler ambiguity solver and a stepped approximation-and-comparison algorithm. After range velocity compensated, the target in the patch is concentrated in less range bins, and its azimuth velocity component is estimated by using an SDFP bank. Finally, the moving target is refocused and its azimuth displacement caused by range velocity component is corrected. The effectiveness of the proposed scheme is confirmed by the experiments with the field and simulated data.

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High Range Resolution Profiles as Motion-Invariant Features for Moving Ground Targets Identification in SAR-Based Automatic Target Recognition

Cited by 37 publications

References 17 publications

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Moving-Target Velocity Estimation in a Complex-Valued Sar Imagery

Ground moving target indication and parameters estimation using a dual-frequency synthetic aperture radar

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