An Improved Indoor Location Algorithm Based on Backpropagation Neural Network

Due to the complexity of indoor environments, there are still some problems in indoor positioning, such as low position calculation efficiency and positioning accuracy. To solve the above two problems, an improved indoor positioning algorithm based on backpropagation neural network (BPNN) is propose...

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Bibliographic Details
Published in:Arabian journal for science and engineering (2011) 2022-11, Vol.47 (11), p.13823-13835
Main Authors: Xie, Yaqin, Wang, Teqi, Xing, Ziling, Huan, Hai, Zhang, Yu, Li, Ye
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation
Back propagation networks
Engineering
Humanities and Social Sciences
Indoor environments
K-nearest neighbors algorithm
multidisciplinary
Neural networks
Research Article-Electrical Engineering
Science
Screening
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Summary:Due to the complexity of indoor environments, there are still some problems in indoor positioning, such as low position calculation efficiency and positioning accuracy. To solve the above two problems, an improved indoor positioning algorithm based on backpropagation neural network (BPNN) is proposed in this paper. Computation efficiency is increased by area partition and area screening; at the same time, positioning accuracy is improved by using BPNN algorithm considering the environment factors. For comparison, an improved K-nearest neighbor (KNN) algorithm based on area partition and area screening is also proposed. Experiment results show that the improved BPNN-based algorithm can obtain better positioning performance compared with both the traditional KNN method and the improved KNN method.
ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-021-06529-z