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Keywords = bistatic inverse synthetic aperture radar (Bi-ISAR)

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15 pages, 2313 KiB  
Article
Research on Bi–ISAR Sparse Aperture High Resolution Imaging Algorithm under Low SNR
by Hanshen Zhu, Wenhua Hu, Baofeng Guo, Liting Jiao, Xiaoxiu Zhu and Chang’an Zhu
Electronics 2022, 11(18), 2856; https://doi.org/10.3390/electronics11182856 - 9 Sep 2022
Cited by 4 | Viewed by 1517
Abstract
In the imaging process of bistatic inverse synthetic aperture radar (Bi–ISAR), the echo is easily affected by the internal interference and external environment of the radar system, resulting in the problems of sparse aperture and low echo signal noise ratio. The efficiency of [...] Read more.
In the imaging process of bistatic inverse synthetic aperture radar (Bi–ISAR), the echo is easily affected by the internal interference and external environment of the radar system, resulting in the problems of sparse aperture and low echo signal noise ratio. The efficiency of conventional sparse aperture imaging methods is greatly reduced. To solve the above problems, a Bi–ISAR sparse aperture imaging algorithm based on Complex Variational Modal Decomposition (CVMD) and wavelet threshold de–noising is proposed. Firstly, the Bi–ISAR sparse aperture echo signal model is established, the sparse basis matching the echo is constructed, the echo signal is decomposed into different intrinsic mode functions (IMF) by CVMD, and the IMFs belonging to the signal are separated by the energy relationship criterion. Then, after the IMFs is de–noising by the improved wavelet threshold de–noising algorithm, the original signal is synthesized, and the signal after de–noising is reconstructed by using the orthogonal matching pursuing algorithm. Simulation results show that the proposed algorithm can achieve Bi–ISAR sparse aperture high−resolution imaging under low signal noise ratio and has fine anti−noise performance. Full article
(This article belongs to the Section Circuit and Signal Processing)
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17 pages, 17359 KiB  
Article
High-Resolution Bistatic ISAR Imaging of a Space Target with Sparse Aperture
by Lin Shi, Xiaoxiu Zhu, Chaoxuan Shang, Baofeng Guo, Juntao Ma and Ning Han
Electronics 2019, 8(8), 874; https://doi.org/10.3390/electronics8080874 - 7 Aug 2019
Cited by 6 | Viewed by 3427
Abstract
Due to the large size of space targets, migration through resolution cells (MTRC) are induced by a rotational motion in high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) systems. The inaccurate correction of MTRC degrades the quality of Bi-ISAR images. However, it is challenging [...] Read more.
Due to the large size of space targets, migration through resolution cells (MTRC) are induced by a rotational motion in high-resolution bistatic inverse synthetic aperture radar (Bi-ISAR) systems. The inaccurate correction of MTRC degrades the quality of Bi-ISAR images. However, it is challenging to correct the MTRC where sparse aperture data exists for Bi-ISAR systems. A joint approach of MTRC correction and sparse high-resolution imaging for Bi-ISAR systems is presented in this paper. First, a Bi-ISAR imaging sparse model-related to MTRC is established based on compress sensing (CS). Second, the target image elements and noise are modeled as the complex Laplace prior, and the Gaussian prior, respectively. Finally, the high-resolution, well-focused image is obtained by the full Bayesian inference method, without manual adjustments of unknown parameters. Simulated results verify the effectiveness and robustness of the proposed algorithm. Full article
(This article belongs to the Special Issue Advanced Antenna Measurement Techniques for Radar, IoT and 5G Applications)
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15 pages, 7517 KiB  
Article
Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm
by Jiyuan Chen, Xiaoyi Pan, Letao Xu and Wei Wang
Sensors 2018, 18(9), 3082; https://doi.org/10.3390/s18093082 - 13 Sep 2018
Cited by 2 | Viewed by 2874
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Section Remote Sensors)
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