Radial motion estimation for pulse stepped-frequency radar

Analysis and Compensation of Radial Motion Effect on Stepped Frequency Signal

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1977 ◽

2013 ◽

Vol 427-429 ◽

pp. 1977-1980

Author(s):

Yao Tian Zhang ◽

Jia Jia Liu ◽

Yu Xian Zhang ◽

Miao Liu

Keyword(s):

Velocity Measurement ◽

Pulse Signal ◽

Radial Motion ◽

Frequency Signal ◽

Motion Effect ◽

Stepped Frequency

Stepped frequency signal is an important high distance resolution signal, this property is realized by combining several pulse signal, so it is easy to be effected by targets radial motion. Here we analyzed how targets radial motion affects the synthesis HRRP, then a method of velocity measurement is provided, then compensation using the predicted velocity.

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A 2-D Space-Variant Motion Estimation and Compensation Method for Ultrahigh-Resolution Airborne Stepped-Frequency SAR With Long Integration Time

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2017.2727060 ◽

2017 ◽

Vol 55 (11) ◽

pp. 6390-6401 ◽

Cited By ~ 13

Author(s):

Jianlai Chen ◽

Mengdao Xing ◽

Guang-Cai Sun ◽

Zhenyu Li

Keyword(s):

Motion Estimation ◽

Compensation Method ◽

Integration Time ◽

Ultrahigh Resolution ◽

Stepped Frequency ◽

Space Variant

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Radial Motion Estimation of Myocardium in Rats with Myocardial Infarction: A Hybrid Method of FNCCGLAM and Polar Transformation

Ultrasound in Medicine & Biology ◽

10.1016/j.ultrasmedbio.2020.08.014 ◽

2020 ◽

Vol 46 (12) ◽

pp. 3413-3425

Author(s):

Xiaomin Liu ◽

Yinong Wang ◽

Peizhen Zhang ◽

Qing Wang ◽

Qianjin Feng ◽

...

Keyword(s):

Myocardial Infarction ◽

Motion Estimation ◽

Hybrid Method ◽

Radial Motion

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OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i01.5475 ◽

2020 ◽

Vol 34 (01) ◽

pp. 1218-1225

Author(s):

Chengqian Zhao ◽

Cheng Feng ◽

Dengwang Li ◽

Shuo Li

Keyword(s):

Carotid Artery ◽

Motion Estimation ◽

Optical Flow ◽

Quantitative Measurement ◽

Comprehensive Analysis ◽

Radial Motion ◽

Superior Performance ◽

Dynamic Complexity ◽

Carotid Wall ◽

Auxiliary Module

Comprehensively analyzing the carotid artery is critically significant to diagnosing and treating cardiovascular diseases. The object of this work is to simultaneously achieve direct quantitative measurement and automated segmentation of the lumen diameter and intima-media thickness as well as the motion estimation of the carotid wall. No work has simultaneously achieved the comprehensive analysis of carotid artery due to three intractable challenges: 1) Tiny intima-media is more challenging to measure and segment; 2) Artifact generated by radial motion restrict the accuracy of measurement and segmentation; 3) Occlusions on diseased carotid walls generate dynamic complexity and indeterminacy. In this paper, we propose a novel optical flow-auxiliary multi-task regression network named OF-MSRN to overcome these challenges. We concatenate multi-scale features to a regression network to simultaneously achieve measurement and segmentation, which makes full use of the potential correlation between the two tasks. More importantly, we creatively explore an optical flow auxiliary module to take advantage of the co-promotion of segmentation and motion estimation to overcome the restrictions of the radial motion. Besides, we evaluate consistency between forward and backward optical flow to improve the accuracy of motion estimation of the diseased carotid wall. Extensive experiments on US sequences of 101 patients demonstrate the superior performance of OF-MSRN on the comprehensive analysis of the carotid artery by utilizing the dual optimization of the optical flow auxiliary module.

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