吉林大学学报(理学版) ›› 2020, Vol. 58 ›› Issue (4): 953-959.

• 计算机科学 • 上一篇    下一篇

基于可信度小波神经网络的多传感器数据融合方法

陈英, 董思羽   

  1. 南昌航空大学 软件学院, 南昌 330063
  • 收稿日期:2019-07-18 出版日期:2020-07-26 发布日期:2020-07-16
  • 通讯作者: 陈英 E-mail:c_y2008@163.com

Fusion Method Based on Credible Wavelet Neural Network for Multisensor Data

CHEN Ying, DONG Siyu   

  1. School of Software, Nanchang Hangkong University, Nanchang 330063, China
  • Received:2019-07-18 Online:2020-07-26 Published:2020-07-16
  • Contact: CHEN Ying E-mail:c_y2008@163.com

摘要: 针对传感器数据的多样性, 提出一种基于小波和神经网络数据融合的改进方法. 首先, 对传感器数据进行预处理; 然后, 用小波和BP神经网络相结合的方法优化数据; 最后, 利用计算传感器可信度对数据进行融合. 传感器数据融合效果对比实验结果表明, 该算法针对数据预处理和数据融合的稳定性和有效性均较好, 融合结果的离散程度优于加权数据融合和Kalman数据融合等方法.

关键词: 多传感器, 数据融合, 可信度, 小波神经网络

Abstract: In view of the diversity of sensor data, we proposed an improved method based on wavelet and neural network data fusion. Firstly, the sensor data was preprocessed. Secondly, wavelet and BP neural network were combined to optimize the data. Finally, the data was fused by calculating the credibility of the sensor. The experimental results of the sensor data fusion effect show that  the algorithm is stable and effective for data processing and data fusion. The degree of dispersion of the fusion result is better than that of weighted data fusion, Kalman data fusion and other methods.

Key words: multi-sensor, data fusion, credibility, wavelet neural network

中图分类号: 

  • TP393