Drag coefficient identification of high-spinning projectile using cubature Kalman filter

Zheng, Yucheng; Guan, Jiancheng; Yi, Wei-Jian

doi:10.1063/5.0043224

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…It was later developed into the extended Kalman filter (EKF) and unscented Kalman filter (UKF). Zheng et al [24] proved that UKF is better than EKF in projectile parameter identification because of unscented transformation (U-transformation). To reduce the computational complexity and improve the accuracy, many other improved KF algorithms have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Wang

Xia

2023

International Journal of Aerospace Engineering

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The firing accuracy of the projectile has a positive relation with aerodynamic parameters. Due to the complex dynamic characteristics of projectiles, there is an overfitting risk when a single extreme learning machine (ELM) is used to identify the aerodynamic parameters of the projectile, and the identification results oscillate transonic region. To obtain the aerodynamic parameters of the projectile accurately, an aerodynamic parameter identification model based on ensemble learning theory and ELM optimized by improved particle swarm optimization is proposed. The improved particle swarm optimization algorithm (IPSO) with an adaptive update strategy is used to optimize the weight and threshold of ELM. Combined with the ensemble learning theory, the improved ELM neural network is regarded as a weak learner to generate a strong learner. The structural parameters of the strong learner were continuously optimized through training, and an aerodynamic parameter identification model of projectile based on ensemble learning theory is obtained. The simulation results show that the introduction of the IPSO and ensemble learning theory enables the model to exhibit excellent generalization ability. The proposed identification model can accurately describe the variation of aerodynamic parameters with the Mach number.

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Section: Introductionmentioning

confidence: 99%

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Wang

Xia

2023

International Journal of Aerospace Engineering

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Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

Gao,

2025

Measurement

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A new method for spinning projectile aerodynamic estimation: Extreme learning machine optimized by adaptive particle swarm

Guan

Xia

2021

AIP Advances

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Aiming at the problem of aerodynamic parameter identification of a spinning projectile, an adaptive particle swarm optimization for the extreme learning machine algorithm is proposed in this paper. The algorithm uses the adaptive particle swarm optimization algorithm to optimize the hidden layer weight and threshold of the extreme learning machine to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The free flight data of the projectile are generated by numerical simulation, and the aerodynamic parameters of a projectile are identified by the proposed algorithm. Simulation results show that the proposed algorithm can effectively identify the aerodynamic parameters of the projectile, and it has high identification accuracy and fast convergence speed. The proposed algorithm is suitable for engineering applications.

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Drag coefficient identification of high-spinning projectile using cubature Kalman filter

Cited by 4 publications

References 5 publications

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Aerodynamic Parameter Identification of Projectile Based on Improved Extreme Learning Machine and Ensemble Learning Theory

Optimizing projectile aerodynamic parameter identification of kernel extreme learning machine based on improved Dung Beetle Optimizer algorithm

A new method for spinning projectile aerodynamic estimation: Extreme learning machine optimized by adaptive particle swarm

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