scholarly journals ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNR

2021 ◽  
Vol 13 (11) ◽  
pp. 2198
Author(s):  
Zhijun Yang ◽  
Dong Li ◽  
Xiaoheng Tan ◽  
Hongqing Liu ◽  
Yuchuan Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Rotational Motion ◽  
Continuous Change ◽  
Signal Model ◽  
Optimal Convergence ◽  
Phase Errors ◽  
Image Entropy ◽  
Isar Imaging ◽  
Gradient Based ◽  
Global Optimal

The existing inverse synthetic aperture radar (ISAR) imaging algorithms for ship targets with complex three-dimensional (3D) rotational motion are not applicable because of continuous change of image projection plane (IPP), especially under low signal-to-noise-ratio (SNR) condition. To overcome this obstacle, an efficient approach based on generalized Radon Fourier transform (GRFT) and gradient-based descent optimal is proposed in this paper. First, the geometry and signal model for nonstationary IPP of ship targets with complex 3-D rotational motion is established. Furthermore, the two-dimensional (2D) spatial-variant phase errors caused by complex 3-D rotational motion which can seriously blur the imaging performance are derived. Second, to improve the computational efficiency for 2-D spatial-variant phase errors compensation, the coarse motion parameters of ship targets are estimated via the GRFT method. In addition, using the gradient-based descent optimal method, the global optimum solution is iteratively estimated. Meanwhile, to solve the local extremum for cost surface obtained via conventional image entropy, the image entropy combined with subarray averaging is applied to accelerate the global optimal convergence. The main contributions of the proposed method are: (1) the geometry and signal model for ship targets with a complex 3-D rotational motion under nonstationary IPP are established; (2) the image entropy conjunct with subarray averaging operation is proposed to accelerate the global optimal convergence; (3) the proposed method can ensure the imaging accuracy even with high imaging efficiency thanks to the sole optimal solution generated by using the subarray averaging and image entropy. Several experiments using simulated and electromagnetic data are performed to validate the effectiveness of the proposed approach.

scholarly journals A Novel 2-D Geometry Reconstruction Approach for Space Debris via Interpolation-Free Operation under Low SNR Conditions

2020 ◽  
Vol 12 (12) ◽  
pp. 2059
Author(s):  
Xi Luo ◽  
Lixin Guo ◽  
Dong Li ◽  
Hongqing Liu ◽  
Mengyi Qin
Keyword(s):  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Low Snr ◽  
Phase Errors ◽  
Isar Imaging ◽  
Key Issues ◽  
Imaging Mode ◽  
Imaging Performance ◽  
Low Computational Cost ◽  
Spinning Targets

Two unsolved key issues in inverse synthetic aperture radar (ISAR) imaging for non-cooperative rapidly spinning targets including high computational complexity and poor imaging performance in the case of low signal-to-noise ratio (SNR) are addressed in this work. In the strip-map imaging mode of SAR, it is well known that azimuth spatial invariant characteristics exist, and inspired by this, we propose a fast ISAR imaging approach for spinning targets. Our approach involves two steps. First, a precise analytic expression in the range-Doppler (RD) domain is produced using the principle of stationary phase (POSP). Second, a novel interpolation kernel function is designed to remove two-dimensional (2-D) spatial-variant phase errors, and the corresponding fast implementation steps that only require Fourier transform and multiplications are also presented. Finally, a well-focused ISAR image is obtained by compensating the azimuth high-order terms. Compared with current imaging methods, our approach avoids multi-dimensional search and interpolation operations and exploits the 2-D coherent integrated gain; the proposed method is of low computational cost and robustness in the low SNR condition. The effectiveness of the proposed approach is confirmed by numerically simulated experiments.

ISAR imaging of target with complex motion associated with the fractional Fourier transform

2018 ◽  
Vol 83 ◽  
pp. 332-345 ◽  
Author(s):  
Hong-Cai Xin ◽  
Xia Bai ◽  
Yu-E Song ◽  
Bing-Zhao Li ◽  
Ran Tao
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Complex Motion ◽  
Isar Imaging

ISAR Imaging of Targets With Complex Motion Based on Discrete Chirp Fourier Transform for Cubic Chirps

2012 ◽  
Vol 50 (10) ◽  
pp. 4201-4212 ◽  
Author(s):  
Liang Wu ◽  
Xizhang Wei ◽  
Degui Yang ◽  
Hongqiang Wang ◽  
Xiang Li
Keyword(s):  
Fourier Transform ◽  
Complex Motion ◽  
Isar Imaging

A Fully Automatic Autofocusing Algorithm for Post-processing ISAR Imaging based on Image Entropy Minimization

1970 ◽  
Vol 110 (4) ◽  
pp. 125-130
Author(s):  
E. D. Kallitsis ◽  
A. V. Karakasiliotis ◽  
G. E. Boultadakis ◽  
P. V. Frangos
Keyword(s):  
Computational Cost ◽  
Angular Acceleration ◽  
Synthetic Aperture ◽  
Post Processing ◽  
Acceptable Range ◽  
Image Entropy ◽  
Isar Imaging ◽  
Fully Automatic ◽  
Simulation Results ◽  
Stepped Frequency

Autofocus is a technique for improving inverse synthetic aperture radar (ISAR) imaging. In this paper, a novel autofocusing method is developed for high-resolution stepped-frequency ISAR. Non-uniform rotational motion is compensated through the proposed post-processing methodology. In this way, the computational cost of polar reformatting process can be circumvented. The proposed CPI-split autofocusing process results in well-focused ISAR images for high angular acceleration periods. Finally, ISAR image entropy dependencies are thoroughly examined through various simulation results, leading to an acceptable range of entropy values for the autofocusing process. Ill. 7, bibl. 9, tabl. 3 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.110.4.305

scholarly journals Study of Rotational Motion Compensation Method Based on PPP for ISAR Imaging

2018 ◽  
Vol 29 (2) ◽  
pp. 109-117 ◽  
Author(s):  
Ki-Bong Kang ◽  
Sang-Hong Park ◽  
Byung-Soo Kang ◽  
Bo-Hyun Ryu ◽  
Kyung-Tae Kim
Keyword(s):  
Motion Compensation ◽  
Rotational Motion ◽  
Compensation Method ◽  
Isar Imaging

scholarly journals Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3082 ◽  
Author(s):  
Jiyuan Chen ◽  
Xiaoyi Pan ◽  
Letao Xu ◽  
Wei Wang
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Super Resolution ◽  
Space Distribution ◽  
Signal Model ◽  
Low Snr ◽  
Dual Channel ◽  
Isar Imaging ◽  
Radar Echo ◽  
Resolution Model

Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.

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