A survey on ISAR autofocusing techniques

Berizzi, F.; Martorella, Marco; Haywood, B.; Mese, E. Dalle; Bruscoli, S.

doi:10.1109/icip.2004.1418676

Cited by 68 publications

(25 citation statements)

References 7 publications

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“…However, presence of strong noise brings inherent difficulty to precise phase tracking through several dominant scatters. Another group numerically optimizes the phase error correction to improve a global metric consistent with image focus, in which image contrast (IC) [13][14][15][16] and entropy [17][18][19][20][21][22] are utilized as the cost function to optimize the phase error. Image metricbased approaches are usually able to obtain an optimal solution even in the presence of strong background noise and clutter.…”

Section: Introductionmentioning

confidence: 99%

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Zhang

Sheng

Duan

et al. 2013

EURASIP J. Adv. Signal Process.

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In general, conventional error correction for inverse synthetic aperture radarimaging consists of range alignment and phase adjustment, which compensate range shift and phase error, respectively. Minimum entropy-based methods have been proposed to realize range alignment and phase adjustment. However, it becomes challenging to align high-resolution profiles when strong noise presents, even using entropy minimization. Consequently, the subsequent phase adjustment fails to correct phase errors. In this article, we propose a novel method for translational motion correction, where entropy minimization is utilized to achieve range alignment and phase adjustment jointly. And, a coordinate descent algorithm is proposed to solve the optimization implemented by quasi-Newton algorithm. Moreover, a method for coarse motion estimation is also proposed for initialization in solving the optimization. Both simulated and real-measured datasets are used to confirm the effectiveness of the joint motion correction in low signal-to-noise ratio situations.

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Section: Introductionmentioning

confidence: 99%

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Zhang

Sheng

Duan

et al. 2013

EURASIP J. Adv. Signal Process.

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“…Small IE values therefore indicate good image quality. The image entropy-based Autofocus (IEBA) uses the IE in a parametric motion estimation algorithm [5]. The central assumption to the IEBA is that the IE value of a resulting ISAR image adopts its minimum if correct radial motion compensation is applied.…”

Section: Conventional Monostatic Algorithmmentioning

confidence: 99%

Multistatic ISAR autofocus with an image entropy-based technique

Brisken

Martella

2014

IEEE Aerosp. Electron. Syst. Mag.

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A key issue in radar research for many years has been the noncooperative identification (NCI) of moving targets. Such targets can be aircraft, ships, ground vehicles, satellites, or ballistic missiles. Nowadays, identification is mostly based on cooperative methods like the identification friend-foe (IFF) systems in the military sector or automatic dependent surveillance-broadcast (ADS-B) for civil aircraft. However, a number of situations exist in which a cooperative identification is not possible. In conflict situations, hostile targets do not identify themselves. Knowledge of their type by NCI yields a tactical advantage. History has also shown a significant number of cases in which neutral or friendly targets have been destroyed due to their inability to identify themselves. Neutral targets usually do not share IFF codes of conflict parties, and friendly targets may turn off their IFF when performing covert operations. Operations between allied nations have also been a source of possible IFF failure. Furthermore, the desire for NCI is not limited to conflict situations. Unidentified aircraft or ships often are suspected to be terrorists. In all of the above situations, a decision on a possible response is extremely time critical, and possible consequences can be serious. For this reason, NCI remains an important research topic

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“…The terms T m R t and R m R t will not benefit the target imaging process and can be compensated through some translational motion compensation methods [7][8][9].…”

Section: A Imaging Geometry Of Moving Targetmentioning

confidence: 99%

Hybrid SAR/ISAR imaging of ground moving target with along-track airborne bi-static SAR

Yeh

Yin

Yang

2011

2011 International Conference on Electronics, Communications and Control (ICECC)

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An along-track airborne bi-static synthetic aperture radar (SAR) configuration is used for ground moving target imaging in this paper. The imaging characteristics are analyzed and two parameters are defined to present the best imaging geometry. What's more, an image rotation correlation method is used to estimate the equivalent rotational motion of a noncooperative moving target, which will benefit the image forming and understanding. At last, numerical simulations are presented to demonstrate the effectiveness of the above analysis.

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A survey on ISAR autofocusing techniques

Cited by 68 publications

References 7 publications

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Translational motion compensation for ISAR imaging under low SNR by minimum entropy

Multistatic ISAR autofocus with an image entropy-based technique

Hybrid SAR/ISAR imaging of ground moving target with along-track airborne bi-static SAR

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