A multi-sensor data fusion algorithm based on consistency preprocessing and adaptive weighting

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 algor...

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Veröffentlicht in:Automatika Jg. 65; H. 1; S. 82 - 91
Hauptverfasser: Du, Shengxue, Chen, Shujun
Format: Journal Article Paper
Sprache:Englisch
Veröffentlicht: Ljubljana Taylor & Francis Ltd 02.01.2024
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Schlagworte:
adaptive weighting
Algorithms
Consistency
consistency preprocess
Data collection
data fusion
Data integration
Errors
INDEX TERMS: Multi-sensor system
Multi-sensor system
Multisensor fusion
off-centre distance
Preprocessing
Redundancy
Sensors
Weighting methods
ISSN:0005-1144, 1848-3380
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
322948
ISSN:0005-1144
1848-3380
DOI:10.1080/00051144.2023.2284033