Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

IF 2.1 3区工程技术 Q2 ENGINEERING, AEROSPACE

Aerospace Pub Date : 2023-12-09 DOI:10.3390/aerospace10121023

Assefinew Wondosen, Yisak Debele, Seung-Ki Kim, Hayoung Shi, Bedada Endale, Beom-Soo Kang

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引用次数: 0

Abstract

In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved.

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用于微调无人飞行器姿态和航向参考系统估计中 EKF 参数的贝叶斯优化方法

在各种应用中，扩展卡尔曼滤波器（EKF）对于估计车辆在三维（3D）空间中的平移和角度运动至关重要。它对于融合来自多个传感器的数据也至关重要。然而，要使 EKF 有效发挥作用，必须正确选择最佳过程噪声协方差矩阵 (Q) 和测量噪声协方差矩阵 (R)。由于需要一种简便的机制来选择 Q 值和 R 值，EKF 的使用一直面临挑战。因此，本研究的重点是开发一种可轻松应用于确定 Q 和 R 的算法，使我们能够充分发挥 EKF 的潜力。因此，为实现这一目标，我们采用了 EKF 创新一致性统计驱动的贝叶斯优化算法。对 Q 值和 R 值进行了调整，直到预期结果符合通过改进测量创新一致性实现最小误差的性能要求。综合结果表明，当使用建议技术调整的最佳 Q 值和 R 值时，EKF 的性能显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Aerospace ENGINEERING, AEROSPACE-

CiteScore

3.40

自引率

23.10%

发文量

661

审稿时长

6 weeks

期刊介绍： Aerospace is a multidisciplinary science inviting submissions on, but not limited to, the following subject areas: aerodynamics computational fluid dynamics fluid-structure interaction flight mechanics plasmas research instrumentation test facilities environment material science structural analysis thermophysics and heat transfer thermal-structure interaction aeroacoustics optics electromagnetism and radar propulsion power generation and conversion fuels and propellants combustion multidisciplinary design optimization software engineering data analysis signal and image processing artificial intelligence aerospace vehicles'' operation, control and maintenance risk and reliability human factors human-automation interaction airline operations and management air traffic management airport design meteorology space exploration multi-physics interaction.