Zhu Ting, Peng Gao, Li Jianping, Xuan Jiawei, Tian Jingbei
School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
Beijing Institute of Control Engineering, Beijing 100094, China.
Micromachines (Basel). 2025 Jun 24;16(7):739. doi: 10.3390/mi16070739.
The weighted averaging algorithm is a widely adopted high-efficiency data fusion approach for micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) array, where the configuration of weighting coefficients plays a critical role in improving measurement accuracy. In this study, an optimal weighted averaging algorithm based on the fruit fly optimization algorithm (FOA) is proposed by analyzing the data fusion mechanism of the MEMS IMU array. Firstly, a measurement model for the MEMS IMU array is constructed, and the principles of data fusion are systematically investigated. Secondly, the optimal weighting coefficients under ideal conditions are derived, and their limitations in practical applications are discussed. Building on this framework, the FOA is employed to search for optimal weights, enabling the realization of high-precision weighted averaging fusion. Simulation and experimental results demonstrate that the proposed method outperforms conventional approaches in terms of accuracy and robustness.
加权平均算法是一种广泛应用于微机电系统（MEMS）惯性测量单元（IMU）阵列的高效数据融合方法，其中加权系数的配置在提高测量精度方面起着关键作用。在本研究中，通过分析MEMS IMU阵列的数据融合机制，提出了一种基于果蝇优化算法（FOA）的最优加权平均算法。首先，构建了MEMS IMU阵列的测量模型，并系统地研究了数据融合原理。其次，推导了理想条件下的最优加权系数，并讨论了它们在实际应用中的局限性。在此框架基础上，采用FOA搜索最优权重，实现高精度加权平均融合。仿真和实验结果表明，该方法在精度和鲁棒性方面优于传统方法。
