Parameters estimation for symmetric spinning projectiles using maximum likelihood method based on interior point algorithm

Wenjian Ying; Shiyan Sun; Xuan Wang

doi:10.1117/12.2639541

27 June 2022 Parameters estimation for symmetric spinning projectiles using maximum likelihood method based on interior point algorithm

Wenjian Ying, Shiyan Sun, Xuan Wang

Author Affiliations +

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122530U (2022) https://doi.org/10.1117/12.2639541
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

Gun lunched projectile often has a challenge problem associated with the dynamic system is the modeling inaccuracies resulting from the rapid changes of atmospheric properties and aerodynamic characteristics at high flight velocity. A maximum likelihood method based on interior point algorithm to estimate the aerodynamic parameters for symmetric spinning projectiles is proposed in this paper. The proposed algorithm can improve the accuracy and efficiency compared with traditional maximum likelihood estimation, and reduce the dependence of initial conditions and keep the aerodynamic estimation results in a reasonable scope for a spinning projectile. Simulation results shows that the algorithm proposed in this paper can effectively identify the aerodynamic parameters with high precision and a quick convergence velocity and is therefore suitable for use in actual engineering.

Citation Download Citation

Wenjian Ying, Shiyan Sun, and Xuan Wang "Parameters estimation for symmetric spinning projectiles using maximum likelihood method based on interior point algorithm", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122530U (27 June 2022); https://doi.org/10.1117/12.2639541

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KEYWORDS

Aerodynamics

Computer simulations

Radar

Signal to noise ratio

Data modeling

Dynamical systems

Mathematical modeling

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