scholarly journals ISAR Imaging of Nonuniformly Rotating Targets With Low SNR Based on Third Order Autocorrelation Function

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74707-74719
Author(s):  
Yanyan Li ◽  
Jiancheng Zhang ◽  
Jibin Zheng ◽  
Jinping Niu ◽  
Yan Zhou ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Third Order ◽  
Low Snr ◽  
Isar Imaging

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.

scholarly journals High-Resolution ISAR Imaging With Sparse Aperture VFM Waveforms Under Low SNR Condition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 110651-110659
Author(s):  
Jiyuan Chen ◽  
Letao Xu ◽  
Xiaoyi Pan ◽  
Pu Zheng ◽  
Shunping Xiao
Keyword(s):  
High Resolution ◽  
Low Snr ◽  
Isar Imaging ◽  
Sparse Aperture

ISAR Imaging of Nonuniformly Rotating Target Based on the Multicomponent CPS Model Under Low SNR Environment

2017 ◽  
Vol 53 (3) ◽  
pp. 1119-1135 ◽  
Author(s):  
Dong Li ◽  
Muyang Zhan ◽  
Xinzheng Zhang ◽  
Zhiping Fang ◽  
Hongqing Liu
Keyword(s):  
Low Snr ◽  
Isar Imaging ◽  
Cps Model ◽  
Rotating Target

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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