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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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1584; no. 1; pp. 12025 - 12030
Main Author Shuai, Liu
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.07.2020
Subjects
Adaptive systems
Algorithms
Back propagation networks
Data integration
Data processing
Error analysis
Error detection
Multisensor fusion
Neural networks
Noise measurement
Physics
Sensors
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1584/1/012025