Open AccessMulti-Sensor Data Fusion Algorithm Based on BP Neural Network
Author(s) -
Shuai Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1584/1/012025
Subject(s) - artificial neural network , sensor fusion , computer science , noise (video) , matlab , algorithm , process (computing) , brooks–iyengar algorithm , fusion , data processing , test data , data mining , artificial intelligence , telecommunications , linguistics , philosophy , wireless network , key distribution in wireless sensor networks , programming language , image (mathematics) , wireless , operating system
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.