Proceedings of the 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016)

Bearing Locating Algorithm of Target based on Radial Basis Function Neural Network

Authors
Zihao Wang, Jie Tian
Corresponding Author
Zihao Wang
Available Online May 2016.
DOI
10.2991/itoec-16.2016.19How to use a DOI?
Keywords
radial basis function neural network; pyroelectric infrared sensor; bearing-location
Abstract

Aiming at the high locating error and the underutilization of redundancy bearing measurement in Pyroelectric Infrared Sensor (PIR) location system, a novel method of bearing location based on Radial Basis Function Neural Network (RBFNN) is presented. After illustrating the region partition model of PIR sensor node, we take advantage of the K-means clustering method and the gradient-descent method to train the neural network. By comparing different sizes of training samples, we select a neural network model with lower locating error, and we have made a comparison of RBFNN and the geometric algorithm. The result of simulation shows that the neural network model has 18% higher locating accuracy and the locating error is much less than the geometric algorithm when the target is near the boundary of the detecting area.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016)
Series
Advances in Engineering Research
Publication Date
May 2016
ISBN
978-94-6252-178-0
ISSN
2352-5401
DOI
10.2991/itoec-16.2016.19How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Zihao Wang
AU  - Jie Tian
PY  - 2016/05
DA  - 2016/05
TI  - Bearing Locating Algorithm of Target based on Radial Basis Function Neural Network
BT  - Proceedings of the 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016)
PB  - Atlantis Press
SP  - 92
EP  - 97
SN  - 2352-5401
UR  - https://doi.org/10.2991/itoec-16.2016.19
DO  - 10.2991/itoec-16.2016.19
ID  - Wang2016/05
ER  -