基于分批估计的自适应加权数据融合算法

doi:10.16180/j.cnki.issn1007-7820.2022.05.004

Abstract

Abstract:

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

SHI Zhenhua,ZHANG Na,BAO Xiaoan,SONG Jie. Adaptive Weighted Data Fusion Algorithm Based on Batch Estimation[J].Electronic Science and Technology, 2022, 35(5): 19-25.

Figures/Tables 8

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Figure 1.

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Table 2.

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Figure 3.

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Table 4.

References 19

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传感器名称	概率	正态分布参数	概率	正态分布参数
传感器1	0.9	(20,4)	0.1	(24,4)
传感器2	0.9	(20,4.5)	0.1	(25,4.5)
传感器3	0.9	(20,5)	0.1	(25,4.5)
传感器4	0.9	(20,5.5)	0.1	(26,5.5)
传感器5	0.9	(20,6)	0.1	(22,6)
传感器6	0.5	(20,6.5)	0.5	(30,6.5)
传感器7	0.9	(20,7)	0.1	(25,7)
传感器8	0.9	(20,7.5)	0.1	(30,7.5)
传感器9	0.9	(20,8)	0.1	(27,8)
传感器10	0.9	(20,8.5)	0.1	(28,8.5)

传感器编号	权值	传感器编号	权值
1	0.195 4	6	0.076 7
2	0.158 3	7	0.071 0
3	0.138 9	8	0.059 5
4	0.103 1	9	0.059 2
5	0.087 3	10	0.050 6

传感器编号	权值	传感器编号	权值
1	0.121 5	6	0.095 2
2	0.092 6	7	0.112 6
3	0.066 1	8	0.080 8
4	0.115 8	9	0.114 7
5	0.113 2	10	0.087 3

方法	均值	均方根误差
均值法	21.06	1.85
自适应加权	20.60	1.46
基于分批自适应加权	20.12	1.32

Adaptive Weighted Data Fusion Algorithm Based on Batch Estimation

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