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

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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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  -