基于分批估计的自适应加权数据融合算法

针对多传感器数据融合问题,文中提出了一种基于分批估计的自适应加权数据融合算法。该算法采用时间序列和空间序列对采集的数据分批求其方差,利用数据一致性检测对噪点进行剔除,进而得到自适应因子。随后采用自适应加权法对数据进行融合,得到预测值。文中模拟物联网数据进行仿真实验。结果表明,在处理数据时运用分批估计的自适应加权多传感器数据融合技术,能够提高传感器测量的精确度和系统的可靠性,基于分批估计的自适应加权平均法比传统自适应方法的均方根误差减少了10%,精度提高了2.3%。

Adaptive Weighted Data Fusion Algorithm Based on Batch Estimation

In this study, an adaptive weighted data fusion algorithm based on batch estimation is proposed for multi-sensor data fusion. The algorithm uses time series and spatial sequences to find the variance of the collected data in batches, and uses data consistency detection to eliminate noise, and then obtains the adaptive factors. Subsequently, the adaptive weighting method is used to fuse the data to obtain the predicted value. The simulation experiments with IoT data show that the adaptive weighted multi-sensor data fusion technology of batch estimation can improve the accuracy of sensor measurement and the reliability of the system when processing data, and the adaptive weighted average method based on batch estimation is 10% less than the root mean square error of traditional adaptive method and the accuracy is improved by 2.3%.