scholarly journals An Efficient Ground Manoeuvring Target Refocusing Method Based on Principal Component Analysis and Motion Parameter Estimation

2020 ◽  
Vol 12 (3) ◽  
pp. 378
Author(s):  
Dong Li ◽  
Haining Ma ◽  
Hongqing Liu ◽  
Zhanye Chen ◽  
Jia Su ◽  
...  

Refocusing ground manoeuvring targets with complex motions in synthetic aperture radar (SAR) remains a challenging objective because of the large range of cell migration (RCM) and time-varying Doppler frequency modulation (DFM). By exploiting the geometric information of RCM and two-dimensional (2-D) coherently integrated gain, a fast ground manoeuvring target refocusing method using principal component analysis (PCA) and high-order motion parameter estimation is proposed. First, an efficient phase difference (PD) method and PCA are utilized to correct the RCM, and then, the energy of the ground manoeuvring target is concentrated into the same range bin. Second, by utilizing the coherently integrated cubic phase function (CICPF) that was developed in our previous work, the motion parameters are obtained accurately, and the manoeuvring target is thus well refocused into a sharp peak point based on the estimated motion parameters. The proposed method is of low computational complexity because it avoids time-consuming search and interpolation operations and demonstrates an improved anti-noise performance due to fully exploiting the 2-D coherent accumulation characteristics for estimating motion parameters and enhanced refocused imaging results for manoeuvring targets due to adopting the high-order motion model. Finally, experiments are conducted using simulated and real SAR data to show the performance of the proposed method.

2018 ◽  
Vol 38 (9) ◽  
pp. 0912006
Author(s):  
郭力仁 Guo Liren ◽  
胡以华 Hu Yihua ◽  
王云鹏 Wang Yunpeng ◽  
徐世龙 Xu Shilong

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3520 ◽  
Author(s):  
Hang Li ◽  
Zhe Zhang ◽  
Xianggen Yin

Because the penetration level of renewable energy sources has increased rapidly in recent years, uncertainty in power system operation is gradually increasing. As an efficient tool for power system analysis under uncertainty, probabilistic power flow (PPF) is becoming increasingly important. The point-estimate method (PEM) is a well-known PPF algorithm. However, two significant defects limit the practical use of this method. One is that the PEM struggles to estimate high-order moments accurately; this defect makes it difficult for the PEM to describe the distribution of non-Gaussian output random variables (ORVs). The other is that the calculation burden is strongly related to the scale of input random variables (IRVs), which makes the PEM difficult to use in large-scale power systems. A novel approach based on principal component analysis (PCA) and high-dimensional model representation (HDMR) is proposed here to overcome the defects of the traditional PEM. PCA is applied to decrease the dimension scale of IRVs and eliminate correlations. HDMR is applied to estimate the moments of ORVs. Because HDMR considers the cooperative effects of IRVs, it has a significantly smaller estimation error for high-order moments in particular. Case studies show that the proposed method can achieve a better performance in terms of accuracy and efficiency than traditional PEM.


2015 ◽  
Vol 9 (6) ◽  
pp. 732-741 ◽  
Author(s):  
Penghui Huang ◽  
Guisheng Liao ◽  
Zhiwei Yang ◽  
Yuxiang Shu ◽  
Wentao Du

2013 ◽  
Vol 21 (10) ◽  
pp. 2656-2663
Author(s):  
李海森 LI Hai-sen ◽  
张艳宁 ZHANG Yan-ning ◽  
姚睿 YAO Rui ◽  
孙瑾秋 Sun Jin-qiu

2021 ◽  
pp. 130463
Author(s):  
Yi-Yang Wu ◽  
Freddy L. Figueira ◽  
Paul H.M. Van Steenberge ◽  
Dagmar R. D'hooge ◽  
Yin-Ning Zhou ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document