Phase compensation for ISAR imaging combined with entropy principle

Qiu, Xiaohui; Zhao, Yifeng; Удпа, Сатиш С.

doi:10.1109/aps.2003.1219822

Cited by 12 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to measure the degree of focus of HRRPs, the entropy function is introduced. Entropy is one of the most suitable functions to represent the sharpness of an image, and it is a widely used principle in ISAR imaging [21]- [24]. It is based on the assumption that if the signal is error-free, the shape of its HRRP will be sharpest which means the energy of HRRP is well-focused.…”

Section: A Minimization Entropy Optimizationmentioning

confidence: 99%

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

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: A Minimization Entropy Optimizationmentioning

confidence: 99%

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

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The entropy of subimage

[ 28 , 29 , 30 ] is defined as

where

is the value of the pixel

. The image entropy reflects the sharpness of the image, and the image with a small entropy value is clearer.…”

Section: Image Fusion Of Nonuniform Rotation Targetmentioning

confidence: 99%

Distributed ISAR Subimage Fusion of Nonuniform Rotating Target Based on Matching Fourier Transform

Zhang

2018

Sensors

View full text Add to dashboard Cite

In real applications, the image quality of the conventional monostatic Inverse Synthetic Aperture Radar (ISAR) for the maneuvering target is subject to the strong fluctuation of Radar Cross Section (RCS), as the target aspect varies enormously. Meanwhile, the maneuvering target introduces nonuniform rotation after translation motion compensation which degrades the imaging performance of the conventional Fourier Transform (FT)-based method in the cross-range dimension. In this paper, a method which combines the distributed ISAR technique and the Matching Fourier Transform (MFT) is proposed to overcome these problems. Firstly, according to the characteristics of the distributed ISAR, the multiple channel echoes of the nonuniform rotation target from different observation angles can be acquired. Then, by applying the MFT to the echo of each channel, the defocused problem of nonuniform rotation target which is inevitable by using the FT-based imaging method can be avoided. Finally, after preprocessing, scaling and rotation of all subimages, the noncoherent fusion image containing all the RCS information in all channels can be obtained. The accumulation coefficients of all subimages are calculated adaptively according to the their image qualities. Simulation and experimental data are used to validate the effectiveness of the proposed approach, and fusion image with improved recognizability can be obtained. Therefore, by using the distributed ISAR technique and MFT, subimages of high-maneuvering target from different observation angles can be obtained. Meanwhile, by employing the adaptive subimage fusion method, the RCS fluctuation can be alleviated and more recognizable final image can be obtained.

show abstract

“…Considering the entropy describes the sharpness of an image, i.e. the smaller the entropy is, the sharper the range profile is [15,16] . So the phase compensation parameters , α β can be obtained as…”

Section: Radar Signal Phase Compensationmentioning

confidence: 99%

High resolution range profile formation based on LFM signal fusion of multiple radars

Wang

et al. 2007

J. of Electron.(China)

View full text Add to dashboard Cite

This paper presents a new method of High Resolution Range (HRR) profile formation based on Linear Frequency Modulation (LFM) signal fusion of multiple radars with multiple frequency bands. The principle of the multiple radars signal fusion improving the range resolution is analyzed. With the analysis of return signals received by two radars, it is derived that the phase difference between the echoes varies almost linearly with respect to the frequency if the distance between two radars is negligible compared with the radar observation distance. To compensate the phase difference, an entropy-minimization principle based compensation algorithm is proposed. During the fusion process, the B-splines interpolation method is applied to resample the signals for Fourier transform imaging. The theoretical analysis and simulations results show the proposed method can effectively increase signal bandwidth and provide a high resolution range profile.

show abstract

Phase compensation for ISAR imaging combined with entropy principle

Cited by 12 publications

References 4 publications

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

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

Distributed ISAR Subimage Fusion of Nonuniform Rotating Target Based on Matching Fourier Transform

High resolution range profile formation based on LFM signal fusion of multiple radars

Contact Info

Product

Resources

About