scholarly journals An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2100
Author(s):  
Binbin Wang ◽  
Hao Cha ◽  
Zibo Zhou ◽  
Huatao Tang ◽  
Lidong Sun ◽  
...  
Keyword(s):  
Simple Procedure ◽  
Phase Error ◽  
Translational Motion ◽  
Minimum Entropy ◽  
New Approach ◽  
Maneuvering Targets ◽  
Quadratic Phase ◽  
Imaging Results ◽  
Isar Imaging ◽  
Maneuvering Motion

Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach.

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

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

2013 ◽  
Vol 2013 (1) ◽  
Author(s):  
Lei Zhang ◽  
Jia-lian Sheng ◽  
Jia Duan ◽  
Meng-dao Xing ◽  
Zhi-jun Qiao ◽  
...  
Keyword(s):  
Motion Compensation ◽  
Translational Motion ◽  
Minimum Entropy ◽  
Low Snr ◽  
Isar Imaging

scholarly journals A Novel Inverse Synthetic Aperture Rada Imaging Method for Maneuvering Targets Based on Modified Chirp Fourier Transform

2018 ◽  
Vol 8 (12) ◽  
pp. 2443 ◽  
Author(s):  
Yakun Lv ◽  
Yongping Wang ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Lei Qiu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Descent Method ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Gradient Descent Method ◽  
Minimum Entropy ◽  
Echo Signal ◽  
Maneuvering Targets ◽  
Imaging Results ◽  
Low Efficiency

When inverse synthetic aperture radar (ISAR) imaging maneuvers targets, the azimuth echo of the target scattering point causes a Doppler frequency time-varying problem, which leads to the blurring and defocusing of the imaging results. Traditional imaging methods struggle to meet the imaging requirements for maneuvering targets due to a poor imaging effect or low efficiency. Given these challenges, a modified chirp Fourier transform (MCFT) imaging method is proposed in this paper, based on the specific relationship between the target rotation parameters and the radar echo signal parameters. Firstly, discrete chirp Fourier transform is used to quickly estimate the target’s coarse rotation ratio. Then, the minimum entropy function and gradient descent method are used to calculate the target’s accurate rotation ratio. Finally, the azimuth focusing image is accomplished by performing MCFT once on the azimuth echo signal using the accurate rotation ratio. This method avoids estimating and separating the sub-echo components one-by-one, considerably improves the imaging speed, and guarantees the best imaging quality by applying the global minimum entropy principle. The experimental results show that the proposed method effectively achieves the two-dimensional, high-quality, and fast imaging of maneuvering targets.

Weighted Minimum Entropy Autofocus Algorithm for ISAR Imaging

2011 ◽  
Vol 33 (8) ◽  
pp. 1809-1815
Author(s):  
Gang Xu ◽  
Lei Yang ◽  
Lei Zhang ◽  
Ya-chao Li ◽  
Meng-dao Xing
Keyword(s):  
Minimum Entropy ◽  
Isar Imaging ◽  
Autofocus Algorithm

A Novel ISAR Imaging Approach for Maneuvering Targets With Satellite-Borne Platform

2021 ◽  
pp. 1-5
Author(s):  
Dong Li ◽  
Jinzhi Ren ◽  
Hongqing Liu ◽  
Zhijun Yang ◽  
Jun Wan ◽  
...  
Keyword(s):  
Maneuvering Targets ◽  
Isar Imaging ◽  
Imaging Approach

scholarly journals A NOVEL METHOD FOR ISAR IMAGING OF MULTIPLE MANEUVERING TARGETS

2019 ◽  
Vol 81 ◽  
pp. 43-54 ◽  
Author(s):  
Jia Zhao ◽  
Yun-Qi Zhang ◽  
Xin Wang ◽  
Sheng Wang ◽  
Feng Shang
Keyword(s):  
Maneuvering Targets ◽  
Isar Imaging ◽  
Novel Method
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