Input Estimation Algorithms for Reentry Vehicle Trajectory Estimation

2005 ◽  
Vol 55 (4) ◽  
pp. 361-375 ◽  
Author(s):  
Cheng-Yu Liu ◽  
Huai-Min Wang ◽  
Pan-Chio Tuan
Keyword(s):  
Trajectory Estimation ◽  
Input Estimation ◽  
Reentry Vehicle ◽  
Estimation Algorithms ◽  
Vehicle Trajectory

High-Speed Reentry Vehicle Trajectory Optimization and Guidance with Lateral Maneuver

2021 ◽  
pp. 4151-4166
Author(s):  
Xiangyang Hui ◽  
Fenghua Chi ◽  
Zheng Qi ◽  
Meng Wu ◽  
Fei Li
Keyword(s):  
Trajectory Optimization ◽  
High Speed ◽  
Reentry Vehicle ◽  
Vehicle Trajectory

scholarly journals Variational Inference With Parameter Learning Applied to Vehicle Trajectory Estimation

2020 ◽  
Vol 5 (4) ◽  
pp. 5291-5298
Author(s):  
Jeremy Nathan Wong ◽  
David Juny Yoon ◽  
Angela P. Schoellig ◽  
Timothy D. Barfoot
Keyword(s):  
Variational Inference ◽  
Parameter Learning ◽  
Trajectory Estimation ◽  
Vehicle Trajectory

scholarly journals Real Time Vehicle Trajectory Estimation on Multiple Lanes

2014 ◽  
Author(s):  
Kristian Kovačić ◽  
Edouard Ivanjko ◽  
Hrvoje Gold
Keyword(s):  
Real Time ◽  
Trajectory Estimation ◽  
Vehicle Trajectory

scholarly journals A Method for Automatic Image Rectification and Stitching for Vehicle Yaw Marks Trajectory Estimation

2016 ◽  
Vol 28 (1) ◽  
pp. 23-30
Author(s):  
Vidas Žuraulis ◽  
Dalius Matuzevičius ◽  
Artūras Serackis
Keyword(s):  
Experimental Investigation ◽  
Measurement Errors ◽  
New Method ◽  
Gps Data ◽  
Image Rectification ◽  
Trajectory Estimation ◽  
Trajectory Reconstruction ◽  
Trajectory Length ◽  
Vehicle Trajectory ◽  
Accident Site

The aim of this study has been to propose a new method for automatic rectification and stitching of the images taken on the accident site. The proposed method does not require any measurements to be performed on the accident site and thus it is frsjebalaee of measurement errors. The experimental investigation was performed in order to compare the vehicle trajectory estimation according to the yaw marks in the stitched image and the trajectory, reconstructed using the GPS data. The overall mean error of the trajectory reconstruction, produced by the method proposed in this paper was 0.086 m. It was only 0.18% comparing to the whole trajectory length.

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