提出一种小波域数据融合模型. 将多个传感器的数据进行多尺度分解，分别对每个尺度上的细节信号和最粗尺度上的近似信号进行局部加权融合，并根据小波重构公式得到原尺度上的融合信号. 基于实际工程应用对该模型进行了数学证明，分析随机序列经离散小波多尺度变换后的形式，研究各尺度上的平滑信号、细节信号之间的统计特性，从理论上解释小波域多尺度数据融合算法的优越性. 实验表明：该方法能显著提高数据融合后微机电陀螺仪的零偏稳定性.

 A wavelet-domain data fusion model is proposed. Data from a set of sensors are decomposed into multiple scales. The details of all scales and the approximation of the most coarse scale are fused with local weights, and the signal is reconstructed from the fused result. This model is confirmed with the mathematical theory based on practical application. Multi-scale wavelet transform of a random sequence is analyzed, and the statistical relations between the smooth signal and detail signal at various scales are studied. Superiority of the wavelet multi-scale data fusion algorithm is shown mathematically. Experimental results show that bias stability of MEMS gyroscope can be improved after data fusion.