Method of Measuring the Ballistic Coefficient of Bullets

2019 ◽  
Vol 51 (5) ◽  
pp. 721-725
Author(s):  
I. B. Chepkov ◽  
A. V. Hurnovych ◽  
S. V. Lapyts’kyi ◽  
V. G. Trofymenko ◽  
O. B. Kuchyns’ka ◽  
...  
2020 ◽  
Vol 29 (1) ◽  
pp. 210-219
Author(s):  
Zhang Wei ◽  
Cui Wen ◽  
Wang Xiuhong ◽  
Wei Dong ◽  
Liu Xing

AbstractDuring re-entry objects with low-eccentricity orbits traverse a large portion of the dense atmospheric region almost every orbital revolution. Their perigee decays slowly, but the apogee decays rapidly. Because ballistic coefficients change with altitude, re-entry predictions of objects in low-eccentricity orbits are more difficult than objects in nearly circular orbits. Problems in orbit determination, such as large residuals and non-convergence, arise for this class of objects, especially in the case of sparse observations. In addition, it might be difficult to select suitable initial ballistic coefficient for re-entry prediction. We present a new re-entry prediction method based on mean ballistic coefficients for objects with low-eccentricity orbits. The mean ballistic coefficient reflects the average effect of atmospheric drag during one orbital revolution, and the coefficient is estimated using a semi-numerical method with a step size of one period. The method is tested using Iridium-52 which uses sparse observations as the data source, and ten other objects with low-eccentricity orbits which use TLEs as the data source. We also discuss the performance of the mean ballistic coefficient when used in the evolution of drag characteristics and orbit propagation. The results show that the mean ballistic coefficient is ideal for re-entry prediction and orbit propagation of objects with low-eccentricity orbits.


Author(s):  
I. Meginnis ◽  
Z. Putnam ◽  
I. Clark ◽  
R. Braun ◽  
G. Barton

2019 ◽  
Vol 56 (3) ◽  
pp. 811-822
Author(s):  
Christopher G. Lorenz ◽  
Zachary R. Putnam

2013 ◽  
Vol 50 (5) ◽  
pp. 1047-1059 ◽  
Author(s):  
Ian M. Meginnis ◽  
Zachary R. Putnam ◽  
Ian G. Clark ◽  
Robert D. Braun ◽  
Gregg H. Barton

2013 ◽  
Vol 753-755 ◽  
pp. 2855-2858 ◽  
Author(s):  
Zhong Liu ◽  
Jing Xin An ◽  
Guo Dong Zhang

It is because of many reasons the trajectory calculated from the theoretical model and the actual trajectory have some error, so the experimental results on the theoretical trajectory must be corrected. In this paper, two degrees of freedom of particle trajectory equations are used to determine the ballistic coefficient. And a SVM Neural Network which has a great learning ability and generalization ability of the extremely small sample is used to adaptive learning the solver deviation of the fit between the trajectory and measured trajectory and amend the ballistic coefficient and modified theoretical trajectory solver results. The test shows that this method has a good precision and stability, and the algorithm can be simple programmed. And it has some value in engineering.


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