Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (5): 19-25.doi: 10.16180/j.cnki.issn1007-7820.2022.05.004

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Adaptive Weighted Data Fusion Algorithm Based on Batch Estimation

SHI Zhenhua1,ZHANG Na1,BAO Xiaoan1,SONG Jie2   

  1. 1. School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou 310018,China
    2. School of Economics,Wuhan University of Technology,Wuhan 430070,China
  • Received:2020-12-22 Online:2022-05-25 Published:2022-05-27
  • Supported by:
    National Natural Science Foundation of China(6207050141);Natural Science Foundation of Zhejiang(LQ20F050010);Research and Development Program of Zhejiang(2020C03094)


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%.

Key words: adaptive weighting, multi-sensor fusion, patch estimation, data fusion, internet of things, numerical consistency detection, time series, spatial sequence

CLC Number: 

  • TP391