Moving Target Indication via RADARSAT-2 Multichannel Synthetic Aperture Radar Processing

Chiu, Shen; Dragosevic, M.V.

doi:10.1155/2010/740130

Cited by 35 publications

(28 citation statements)

References 33 publications

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“…Equation (8) indicates that the image of the moving target is smeared approximately in 1 + |α m |λR 0 /(2ρ 2 a ) azimuth resolution cells when its azimuth returns are compressed by (5). On the contrary, if the azimuth matched filter focuses the image of the moving target ideally, the image of background will be smeared by the same length as ρ smear .…”

Section: Radar Imaging Of a Moving Targetmentioning

confidence: 99%

“…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%

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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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Section: Radar Imaging Of a Moving Targetmentioning

confidence: 99%

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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“…In past years, SAR technology was developed for ground moving target indication (GMTI) applications [1][2][3][4]. In addition to the military applications of the SAR moving target data, there is a utilization for civil projects, such as traffic monitoring and traffic guidance systems.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

Rahmanizadeh

Amini

2017

Remote Sensing

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SAR (Synthetic Aperture Radar) raw data simulation has a significant role in the evaluation of newly-proposed methods for the estimation of moving target parameters. The evaluation of methods in different cases emphasizes the importance of the need to have fast simulators. Using reverse SAR imaging methods for the raw data simulations has achieved good results in the simulation of the static targets, but for the simulation of moving targets these methods have a few shortcomings. In this paper, we propose a method to simulate a speckled scene with moving targets in the hybrid domain. First, the scene is simulated, including speckle, which is statistically in accordance with real SAR image behavior. Then, a reverse imaging algorithm (inverse chirp scaling) was applied on the scene to generate the SAR raw data. The moving target simulation was also done in the time-domain as the next step. Finally, the results from two prior steps were superposed to generate the SAR raw data with moving targets. All steps of the proposed method were evaluated separately. The speckle procedure was evaluated by comparing the speckled SAR image before the simulation and the image of the SAR simulated raw data. The results show similar variations in real and imaginary parts of these data. The correlation between the reflectivity map and the SAR images after the simulation was calculated and the obtained correlation coefficient was about 0.95 for different images. The final data were further analyzed for the displacement of moving targets' positions. The results show similar displacement between moving target SAR raw data with a background and without a background.

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“…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%

“…Traditional velocity estimators use multi-antenna systems, such as linear antenna array velocity SAR (VSAR) [14], along-track interferometry [4,10,[15][16][17], and displaced-phase-center-antenna [8,18]. They can give accurate velocity estimate and resolve the velocity ambiguity well.…”

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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Moving Target Indication via RADARSAT-2 Multichannel Synthetic Aperture Radar Processing

Cited by 35 publications

References 33 publications

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

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

An Integrated Method for Simulation of Synthetic Aperture Radar (SAR) Raw Data in Moving Target Detection

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

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