Influence on rocket fall points distribution due to random wind

Ye, Jun; Wang, Xin; Li, Junmiao

doi:10.1109/iciea.2009.5138520

Cited by 1 publication

(1 citation statement)

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“…Yuan [ 24 ] proposed an N-point adaptive initialization method based on goodness of fit and statistical significance tests to compensate for fuzzy controllability between wind resistance and thrust in a ballistic dynamic model. Yao [ 25 ] developed an engineering model of wind, conducted randomization processes, and employed the Monte Carlo method for theoretical analysis to ascertain the impact of various parameters representing environmental factors on the distribution of rocket falling points. Additionally, MIHAI Lungu [ 26 ] verified the accuracy of autonomous driving considering wind shear and sensor errors via numerical simulations, while Norn Coleman [ 27 ] devised factorized implementations of extended Kalman filtering, smoothing, and prediction algorithms, discussing different wind models and validating the algorithms through simulations.…”

Section: Related Workmentioning

confidence: 99%

A Study on Predicting the Deviation of Jet Trajectory Falling Point under the Influence of Random Wind

Cheng,

Zhu,

Wang

et al. 2024

Sensors

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As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing.

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