Original scientific paper
https://doi.org/10.1080/00051144.2023.2284033
A multi-sensor data fusion algorithm based on consistency preprocessing and adaptive weighting
Shengxue Du
; School of Information and Electrical Engineering, Hebei University of Engineering, Handan, People’s Republic of China
Shujun Chen
; Modern Education Technology Center, Hebei University of Engineering, Handan, People’s Republic of China
*
* Corresponding author.
Abstract
In the data collection of a multi-sensor system, there are problems with large errors, conflicts,
and redundancy. To solve the above problem, a multi-sensor data fusion algorithm based on
anomaly data preprocessing and adaptive weighted estimation is proposed. To improve the reliability of the algorithm, first, for a single sensor measurement signal sequence, a consistency
preprocessing using the off-centre distance method is performed, and the weighting factor of
each measurement data is calculated. Then, the measurement signal sequence is weighted and
fused; Secondly, in response to the uneven distribution of measurement errors among multiple
sensors in different directions, an adaptive weighted data fusion method based on the principle of optimal weight allocation is proposed. The proposed method was compared with the
adaptive weighting method and arithmetic mean method. The simulation results showed that
the total mean square error of the data fusion results obtained using the proposed algorithm
is smaller. The proposed algorithm can effectively improve the accuracy of data measurement,
reduce redundancy, and improve the stability of data measurement.
Keywords
Multi-sensor system; off-centre distance; consistency preprocess; adaptive weighting; data fusion
Hrčak ID:
322948
URI
Publication date:
21.11.2023.
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