Robust autofocus algorithm for ISAR imaging of moving targets

Li, Jian; Wu, Renbiao; Chen, V.C.

doi:10.1109/7.953256

Cited by 85 publications

(5 citation statements)

References 30 publications

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“…Owing to its all‐weather imaging capability, it has been widely used in military and civilian fields. To improve the imaging quality or extract more information, spatially variant apodisation [1–3], relaxation (RELAX) [4–7], CLEAN [8–19] and other methods are commonly used to enhance images.…”

Section: Introductionmentioning

confidence: 99%

“…The CLEAN algorithm in Ref. [11] extracts parameters in the frequency domain before motion compensation for ISAR images. The signal model with a quadratic phase is used to obtain the PSF based on the application of the 2D Fourier transform (FT), instead of directly using the PSF model for scatterer extraction in the image domain [12–14].…”

Section: Introductionmentioning

confidence: 99%

“…An accurate PSF mathematical expression model is a prerequisite for fast and successful implementation of the CLEAN algorithm. Owing to the differences in imaging targets, pre-processing methods and processing domains in which the CLEAN method is executed, PSF model variations exist [11][12][13][14][15][16][17][18][19]. The CLEAN algorithm in Ref.…”

mentioning

confidence: 99%

“…The CLEAN algorithm in Ref. [11] extracts parameters in the Zhang Xiongkui and Cui Jing these authors should be considered co-first authors.…”

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confidence: 99%

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Parametric scatterer extraction method for space‐target inverse synthetic aperture radar image CLEAN

Zhang

Cui

Wang

et al. 2023

IET Radar Sonar & Navi

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Owing to the time variance of the inverse synthetic aperture radar (ISAR) imaging plane of space targets, scatterers with large heights relative to the imaging plane migrate through several Doppler cells. This defocussing in the azimuth direction of each scatterer cannot be compensated via the traditional migration through the resolution cell (MTRC) compensation method because the height of each scatterer is quite different and independently distributed in the ISAR image. In this study, a parametric azimuth-defocussing model-based CLEAN method is proposed for the space-target ISAR image. First, highorder defocus parameters and their influences on the image of space targets were analysed at different arc segments. Subsequently, a mathematical azimuth-defocussing expression of the ISAR image scatterers was derived after MTRC and azimuth high-order phase compensation, and an approximate defocus expression based on the Fresnel integral was provided for computational efficiency. This model can estimate the complex amplitude, coordinates and defocus parameters of each scatterer in the image domain by applying a sequential quadratic programing algorithm. Finally, the validity and stability of the proposed algorithm for the isolated and coupled scatterer extraction were verified at different signal-to-noise ratios, and different defocus parameters were verified through simulations. K E Y W O R D Sinverse synthetic aperture radar (ISAR), parameter estimation, radar imaging, radar resolution | INTRODUCTIONAn inverse synthetic aperture radar (ISAR) uses a synthetic aperture formed by the relative rotation between the target and radar to obtain a high-resolution image of the target. Owing to its all-weather imaging capability, it has been widely used in military and civilian fields. To improve the imaging quality or extract more information, spatially variant apodisation [1-3], relaxation (RELAX) [4][5][6][7], and other methods are commonly used to enhance images.The CLEAN algorithm was first proposed by astronomers [8] and was later adopted in radar imaging to reduce the interference of sidelobes and speckle artefacts [9]. In recent years, the CLEAN algorithm has been widely used in ISAR imaging. As a deconvolution signal-processing technique, the CLEAN algorithm is usually used for feature extraction of all scatterers in an image [10], wherein it iteratively estimates the point-spread function (PSF) of the strongest scatterer and subtracts it from the image. The PSF of the scatterers can be assumed to be an ideal two-dimensional (2D) sinc function when its high-order phase has been compensated. However, this assumption fails in many realistic scenarios. An accurate PSF mathematical expression model is a prerequisite for fast and successful implementation of the CLEAN algorithm. Owing to the differences in imaging targets, pre-processing methods and processing domains in which the CLEAN method is executed, PSF model variations exist [11][12][13][14][15][16][17][18][19]. The CLEAN algorithm in Ref. [11] extracts parameters ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The CLEAN algorithm in Ref. [11] extracts parameters in the Zhang Xiongkui and Cui Jing these authors should be considered co-first authors.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Parametric scatterer extraction method for space‐target inverse synthetic aperture radar image CLEAN

Zhang

Cui

Wang

et al. 2023

IET Radar Sonar & Navi

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“…The traditional envelope alignment method is still suitable for maneuvering targets. When the radar resolution is not high, the rotation of the target in a short time can be regarded as uniform rotation, and a good image can be obtained through a traditional phase compensation technology, such as phase gradient autofocus (PGA) [29]. However, due to the maneuvering characteristics of the maneuvering target, the traditional phase compensation methods are not very effective.…”

Section: Introductionmentioning

confidence: 99%

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

et al. 2023

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High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.

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SAR Imaging: An Autofocusing Method for Improving Image Quality and MFS Image Classification Technique

Malamou

Pandis

Karakasiliotis

et al. 2014

Applications of Mathematics and Informatics in Science and Engineering

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Robust autofocus algorithm for ISAR imaging of moving targets

Cited by 85 publications

References 30 publications

Parametric scatterer extraction method for space‐target inverse synthetic aperture radar image CLEAN

Parametric scatterer extraction method for space‐target inverse synthetic aperture radar image CLEAN

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

SAR Imaging: An Autofocusing Method for Improving Image Quality and MFS Image Classification Technique

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