Multi-Sensor Data Fusion Algorithm Based on BP Neural Network

In multi-sensor detection system, the application of multi-sensor accurate detection system parameters is limited due to the existence of measurement noise. Using multi-source data fusion technology can be more accurate, timely detection and data processing system. Data fusion is a basic function in...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1584; H. 1; S. 12025 - 12030
1. Verfasser: Shuai, Liu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.07.2020
Schlagworte:
Adaptive systems
Algorithms
Back propagation networks
Data integration
Data processing
Error analysis
Error detection
Multisensor fusion
Neural networks
Noise measurement
Physics
Sensors
ISSN:1742-6588, 1742-6596
Online-Zugang:Volltext
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Zusammenfassung:In multi-sensor detection system, the application of multi-sensor accurate detection system parameters is limited due to the existence of measurement noise. Using multi-source data fusion technology can be more accurate, timely detection and data processing system. Data fusion is a basic function in humans and other biological systems. In this paper, in order to make the system adaptive multi-source data fusion, using the BP neural network algorithm is a good way to deal with incomplete test data and test the noise problem. In this paper, the characteristics of three levels of data fusion and the derivation process of BP neural network algorithm are introduced in detail. In order to verify the role of BP neural network algorithm in the process of detection system filtering, a MATLAB simulation experiment is carried out. The experimental results show that the BP neural network algorithm can effectively reduce the measurement error of multi-sensor detection system and improve the detection accuracy.
Bibliographie:ObjectType-Article-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1584/1/012025