轻量化卡尔曼滤波器提升姿态系统计算效率
A Lightweight Cubature Kalman Filter for Attitude and Heading Reference Systems Using Simplified Prediction Equations
- 简化预测方程降低计算开销
- 计算时间减少15%-19%且精度不变
- 适合嵌入式设备实时姿态估计
姿态与航向参考系统（AHRS）在需要可靠姿态与运动感知的场景中广泛应用。本文提出一种改进的立方体卡尔曼滤波器（CKF），称为“Kaisoku立方体卡尔曼滤波器（KCKF）”，在保持估计精度的前提下显著降低计算成本。KCKF通过展开并简化CKF中的求和项，推导出轻量化的预测方程，同时保留等效数学关系。实验表明，相比标准CKF，KCKF在高性能计算机上可减少约19%的计算时间，在低成本单板计算机上可减少约15%的计算时间，且姿态估计精度保持一致。
原文摘要 · Abstract (English)
Attitude and Heading Reference Systems (AHRSs) are broadly applied wherever reliable orientation and motion sensing is required. In this paper, we present an improved Cubature Kalman Filter (CKF) with lower computational cost while maintaining estimation accuracy, which is named "Kaisoku Cubature Kalman Filter (KCKF)". The computationally efficient equations of the KCKF are derived by simplifying those of the CKF, while preserving equivalent mathematical relations. The lightweight prediction equations in the KCKF are derived by expanding the summation terms in the CKF and simplifying the result. This paper shows that the KCKF requires fewer floating-point operations (FLOPs) than the CKF. The controlled experimental results show that the KCKF reduces the computation time by approximately 19% compared to the CKF on a high-performance computer, whereas the KCKF reduces the computation time by approximately 15% compared to the CKF on a low-cost single-board computer. In addition, the KCKF maintains the attitude estimation accuracy of the CKF.
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