Research Into Sar Imaging of Moving Ship Target Based on Isar Technology

Yu, Lei; Li, Chunsheng; Wang, Pengbo; Fang, Yue; Feng, Xin

doi:10.1109/igarss.2018.8517618

Cited by 2 publications

(2 citation statements)

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“…Therefore, we will continue to study methods for simultaneous image processing and phase estimation compensation to improve computing efficiency for processing sub-meter resolution SAR data. To summarize, the use of the proposed and ISAR methods [26] in existing GF-3 SAR ground processing systems could have significant benefits for marine users who need high-precision images of moving marine vessels in applications such as vessel identification and intelligent feature extraction.…”

Section: Discussionmentioning

confidence: 99%

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Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization

Chen

et al. 2020

Remote Sensing

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To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO.

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Section: Discussionmentioning

confidence: 99%

“…Figure 4a,b show the results of the stationary and moving vessels using the CS algorithm. Figure 4c shows the refocusing result of the moving vessel using ISAR processing algorithms [26]. Figure 4d,e show the imaging results of the moving vessel using the classical PSO and co-evolutionary PSO algorithms, respectively.…”

Section: Experimental Testmentioning

confidence: 99%