Improvement of the signal-to-noise ratio in static-mode down-looking synthetic aperture imaging ladar

2015 ◽  
Author(s):  
Zhiyong Lu ◽  
Jianfeng Sun ◽  
Ning Zhang ◽  
Yu Zhou ◽  
Guangyu Cai ◽  
...  
2016 ◽  
Vol 36 (8) ◽  
pp. 0828001
Author(s):  
张宁 Zhang Ning ◽  
卢智勇 Lu Zhiyong ◽  
孙建锋 Sun Jianfeng ◽  
周煜 Zhou Yu ◽  
栾竹 Luan Zhu ◽  
...  

2017 ◽  
Vol 15 (10) ◽  
pp. 102801 ◽  
Author(s):  
Guo Zhang Guo Zhang ◽  
Jianfeng Sun Jianfeng Sun ◽  
Yu Zhou Yu Zhou ◽  
Zhiyong Lu Zhiyong Lu ◽  
Guangyuan Li Guangyuan Li ◽  
...  

2020 ◽  
Author(s):  
Yue Lu ◽  
Jian Yang ◽  
Yue Zhang ◽  
Shiyou Xu

Abstract Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper, a novelanti-noise range alignment approach is proposed. In this new method, the target motion is modelled as a uniformly accelerated motion during a short sub-aperture time. Minimum entropy optimization is implemented to estimate the motion parameters in each sub-aperture. These estimated parameters can be used to align the profiles of the current sub-aperture. Once the range profiles of eachsub-aperture are aligned, the non-coherent accumulation gain is obtained by averaging all profiles in each sub-aperture, which can be used as valuable information. The accumulation and correlation method is applied to align the average range profiles of each sub-aperture because the former step focuses mainly on alignment within the sub-apertures. Experimental results based on simulated and real measured data demonstrate the effectiveness of the proposed algorithm in low signal-to-noise ratio scenarios.


2014 ◽  
Vol 8 (1) ◽  
pp. 083635 ◽  
Author(s):  
Jin Zhang ◽  
Zhiping Li ◽  
Cheng Zheng ◽  
Xianxun Yao ◽  
Baohua Yang ◽  
...  

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