Noise-Robust Motion Compensation for Aerial Maneuvering Target ISAR Imaging by Parametric Minimum Entropy Optimization

Wang, Jiadong; Zhang, Lei; Du, Lan; Yang, Dongwen; Chen, Bin

doi:10.1109/tgrs.2018.2890098

Cited by 49 publications

(12 citation statements)

References 31 publications

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“…We use a motion blur detection method when the camera moves fast, and the global motion is found from the time duration of motion blur frames. To detect motion blur and feature tracking, the motion blur detection method should be reliable and accurate [34]. Moshe Ben-Ezra et al proposed a motion blur detection method using the hybrid camera system and point spread function (PSF) calculation to detect motion blur and improve the accuracy of the object tracking algorithm [35].…”

Section: Related Workmentioning

confidence: 99%

“…The current and previous time steps are denoted as t and t − 1, respectively, while image entropy at the current and previous time steps is E t I and E t−1 I , respectively. In general, as an example of using the variation of entropy for motion blur detection, Jiadong et al proposed a noise robust motion compensation method using parametric minimum entropy optimization [34]. Shuigen et al proposed a gradient magnitude distribution based no-reference image blur assessment method [41].…”

Section: Gradient Of the Entropy Sensormentioning

confidence: 99%

See 1 more Smart Citation

Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Kim

Lukežič

Lee

et al. 2020

Sensors

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Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.

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Section: Related Workmentioning

confidence: 99%

Section: Gradient Of the Entropy Sensormentioning

confidence: 99%

Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Kim

Lukežič

Lee

et al. 2020

Sensors

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“…After the well-established dechirping technique [17], the signal echo becomes: The causes of the intra-pulse modulation phase error have been analyzed in the section I. In this subsection, the proposed phase error model will be discussed, and the impact of the error parameters on ISAR imaging will also be given.…”

Section: A Isar Imaging Geometry and Dechirped Signal Modelmentioning

confidence: 99%

Improved Intra-Pulse Modulation Phase Calibration Algorithm With Accelerated Entropy Minimization Optimization

Zhang

et al. 2019

IEEE Access

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Intra-pulse modulation phase calibration is necessary in inverse synthetic aperture radar (ISAR) imaging of high-speed targets. Traditional intra-pulse phase error compensation strategies rarely handle the high-order and slow-time-variant phase components induced during the coherent processing interval. In this paper, a novel intra-pulse modulation phase calibration with a two-dimensional (2-D) parametric phase model is proposed. It models the intra-pulse phase errors as a 2-D time-variant polynomial with accommodation of both fast-time and slow-time modulation. Entropy minimization of high-resolution range profiles (HRRPs) is developed to retrieve the phase error parameters. Improved coordinate descent optimization solver is established by Levenberg-Marquardt (LM) algorithm in order to find the global optimum of entropy efficiently. Comparative experiments using both simulated and real measured data are performed to demonstrate the enhancements of the proposed algorithm. INDEX TERMS Entropy minimization, intra-pulse modulation, inverse synthetic aperture radar (ISAR), phase error compensation.

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“…By referring to [19] and [20], it is likely that ISAR signals are interfered with by strong noises because of the long observation distance and low transmitting energy. For example, when the target of interset is high-orbit satellite, the SNR of echoes can be very low.…”

Section: Introductionmentioning

confidence: 99%

Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profiles Correlation

Yang

Zhang

2020

Preprint

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Abstract Range alignment is an essential procedure in translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novel anti-noise range alignment approach is proposed. In this new method, the target's motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once each sub-aperture's range profiles are aligned, the noncoherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture for the reason that the former step mainly focuses on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.

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Noise-Robust Motion Compensation for Aerial Maneuvering Target ISAR Imaging by Parametric Minimum Entropy Optimization

Cited by 49 publications

References 31 publications

Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Improved Intra-Pulse Modulation Phase Calibration Algorithm With Accelerated Entropy Minimization Optimization

Noise-Robust Range Alignment Method for Inverse Synthetic Aperture Radar Based on Aperture Segmentation and Average Range Profiles Correlation

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