Fruit-Fly-Optimized Weighted Averaging Algorithm for Data Fusion in MEMS IMU Array

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.