scholarly journals High-Resolution ISAR Imaging with Modified Joint Range Spatial-Variant Autofocus and Azimuth Scaling

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5047
Author(s):  
Jiaqi Wei ◽  
Shuai Shao ◽  
Hui Ma ◽  
Penghui Wang ◽  
Lei Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Motion Compensation ◽  
Rotational Velocity ◽  
Real Data ◽  
Minimum Entropy ◽  
Signal Model ◽  
Compensation Methods ◽  
Isar Imaging ◽  
Center Position ◽  
Joint Range

Well-focused and accurately scaled high-resolution inverse synthetic aperture radar (ISAR) images provide a sound basis for feature extraction and target recognition. This paper proposes a novel high-resolution ISAR imaging algorithm, namely modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS). After motion compensation, the shift of the equivalent rotational center (ERC) of the target destroys the linear relationship between the azimuth chirp rates (ACR) of echo signals and the range coordinates of scattering points, thereby leading to the failure of azimuth scaling. Accordingly, a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established, serving as the basis of MJAAS. By recourse to the Davidon-Fletcher-Powell (DFP) algorithm, MJAAS can jointly estimate the ERCP and ERV by solving a minimum entropy optimization problem, so as to simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus, which further improves the image focusing performance. MJAAS is not restricted by the modes of motion errors (coherent or non-coherent) and the motion compensation methods, so it can be widely applied to real data with the advantages of strong practicality and high accuracy. Extensive experimental results based on both simulated and real data are provided to corroborate the effectiveness of the proposed algorithm.

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 New Rang-Instantaneous Doppler ISAR Imaging Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 682-687
Author(s):  
Bao Ping Wang ◽  
Chao Sun ◽  
Jun Jie Guo
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Motion Compensation ◽  
Noise Suppression ◽  
Phase Error ◽  
Doppler Imaging ◽  
Imaging Algorithm ◽  
Time Frequency ◽  
Maneuvering Target ◽  
Isar Imaging

ISAR imaging algorithm based on sparse representation has the advantages of high resolution, noise suppression and dealing with gapped data effectively. The method is based on the hypothesis that the imaging targets move smoothly. But the movement of ISAR imaging targets is usually of high maneuverability, which results in big phase error after motion compensation. Using the traditional RD imaging algorithm and the imaging algorithm based on sparse representation will make the resultant image fuzzy, and can't even be identified. This paper introduces a new range- instantaneous Doppler imaging algorithm based on sparse representation and time-frequency transform, which can effectively image the maneuvering target. The experimental results validate the feasibility of this approach.

scholarly journals High-Resolution ISAR Imaging with Wideband V-FM Waveforms

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Dejun Feng ◽  
Xiaoyi Pan ◽  
Guoyu Wang
Keyword(s):  
High Resolution ◽  
Motion Compensation ◽  
Synthetic Aperture ◽  
Range Resolution ◽  
Dual Channel ◽  
Scattering Center ◽  
Isar Imaging ◽  
Chirp Signals ◽  
2D Array ◽  
High Range

V-FM waveforms, composed of two chirp signals with the opposite slopes, can also achieve high range resolution with wide bandwidth via intrapulse frequency modulation. In this paper, a framework for inverse synthetic aperture radar (ISAR) imaging of moving targets with V-FM waveforms is investigated, where the range compression of the received signals is achieved by the dual-channel dechirping and the azimuth compression is done via the traditional Fourier transform (FT). The two corresponding reconstructed temporary high-resolution range profiles (HRRPs) from the double channels are synthesized for the HRRPs of the target, in which one is flipped from left to right and added to the other. Then the final HRRPs are arranged into a two-dimensional (2D) array and the azimuth compression is done via FT to achieve the ISAR imaging after the motion compensation. Simulated trials, adopting the scattering center modeling of the Yak-42 plane, are used to validate the correctness of the analyses and the finally well-focused images greatly support the effectiveness of V-FM waveforms in ISAR imaging.

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

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.

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