Statistical CLEAN Technique for ISAR Imaging

Martorella, Marco; Acito, N.; Berizzi, F.

doi:10.1109/tgrs.2007.897440

Cited by 89 publications

(37 citation statements)

References 8 publications

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“…6 show that a more focussed ISAR image can be obtained by using the ω − k inversion, which represent the ad-hoc inversion for this Spotlight SAR data. It is worth noting that, although the difference in terms of IC does not lead to very evident differences in the ISAR image magnitudes (from a visual inspection point of view), phase errors may be present that would destroy useful information for phase-related applications/post processing, such as interferometry [9], cross-range scaling [5], super-resolution [10], [11] and so on. • the same conclusions as above may be drawn regarding the use of a time-widowing algorithm.…”

Section: B Resultsmentioning

confidence: 99%

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing

Berizzi

Giusti

Bacci

2011

2011 IEEE RadarCon (RADAR)

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Moving target often appear defocussed in SAR images due to their relative motion with respect to the SAR center scene. In several applications that require target classification and/or recognition an unfocussed image of a target would likely lead to misclassification, therefore decreasing classification performances. In this paper, the authors present a new method for refocussing moving targets starting from formed Single Look Complex (SLC) SAR images. Results obtained by applying the proposed techique to Cosmo Skymed (CSK) SLC SAR images show its effectiveness.

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Section: B Resultsmentioning

confidence: 99%

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing

Berizzi

Giusti

Bacci

2011

2011 IEEE RadarCon (RADAR)

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“…Clearly, the threshold is adjusted with SNR adaptively, and because the energy-based threshold is independent of the statistics of clutter noise, it should be applied well in different situations. Even so, in some extremely low SNR cases, it is helpful to take the statistics of clutter noise into account, and other threshold with CFAR [29] in radar iamging are supportive to select strong scattering cells.…”

Section: Sdd-cs Processing Scheme For Wideband Rpri Radarmentioning

confidence: 99%

High Resolution Wideband Imaging of Fast Rotating Targets Based on Random Pri Radar

Liu¹,

Chen²,

Sui³

2018

PIER M

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Abstract-By exploiting the micro-motion features of fast rotating targets, wideband radar has been successfully applied to high resolution imaging. However, due to the traditional fixed pulse repetition interval (PRI), the target image may suffer from aliasing in some practical situations. In this paper, under the compressed sensing (CS) radar framework, an efficient wideband imaging scheme with random PRI signal is introduced for aliasing reduction. Considering that direct application of the CS theory will result in large-scale dictionaries and high computational complexity, we firstly generate a low resolution image by applying modified generalized Radon transform on range-slow time domain and then scale down the dictionary column by reserving the atoms corresponding to those strong scattering areas. Simulation results show that this scheme can achieve aliasing-free images with acceptable computational cost.

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“…The maximum likelihood estimate of and can be obtained by (33) We first assume that is known, then the amplitude can be obtained by (34) The maximum likelihood estimate of and can be implemented using the steepest descent algorithm [40]. Denote the gradient of on parameters and be and respectively.…”

Section: Maximum Likelihood Estimation and Steepest Descent Implementioning

confidence: 99%

“…Although sparse array beamforming when combined with ISAR imaging can reduce the sidelobe levels [26], it still requires a sufficiently large number of antenna elements in order to provide an acceptable image. Alternatively, a target can be modeled as a composition of strong scatterers and the CLEAN technique can then be used to extract strong scatterers for subsequent artifacts removal [30]- [34]. However, due to the high sidelobes, the precision of amplitude estimation is low, while the precision of scatterers' position parameters estimation is, at best, moderate.…”

Section: Introductionmentioning

confidence: 99%

Sparse Array 3-D ISAR Imaging Based on Maximum Likelihood Estimation and CLEAN Technique

Yeo

Tan

et al. 2010

IEEE Trans. on Image Process.

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Large 2-D sparse array provides high angular resolution microwave images but artifacts are also induced by the high sidelobes of the beam pattern, thus, limiting its dynamic range. CLEAN technique has been used in the literature to extract strong scatterers for use in subsequent signal cancelation (artifacts removal). However, the performance of DFT parameters estimation based CLEAN algorithm for the estimation of the signal amplitudes is known to be poor, and this affects the signal cancelation. In this paper, DFT is used only to provide the initial estimates, and the maximum likelihood parameters estimation method with steepest descent implementation is then used to improve the precision of the calculated scatterers positions and amplitudes. Time domain information is also used to reduce the sidelobe levels. As a result, clear, artifact-free images could be obtained. The effects of multiple reflections and rotation speed estimation error are also discussed. The proposed method has been verified using numerical simulations and it has been shown to be effective.

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Statistical CLEAN Technique for ISAR Imaging

Cited by 89 publications

References 8 publications

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing

High Resolution Wideband Imaging of Fast Rotating Targets Based on Random Pri Radar

Sparse Array 3-D ISAR Imaging Based on Maximum Likelihood Estimation and CLEAN Technique

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