Research on Vehicle Classification Algorithm Based on Information Fusion of Dual Reluctance Sensor

DOI

10.2991/cnct-16.2017.101How to use a DOI?

Keywords

Anisotropic Magneto ResistanceSensor, Information Fusion, Maximum Likelihood Estimation, Vehicle Classification

Abstract

In view of some errors existing in the vehicle detection system of single sensor node, a vehicle detection algorithm based on dual sensor information fusion for vehicle classification is proposed. The single sensor signal is detected by double threshold method, then matching the vehicle information detected from different sensor nodes within the network. Finally, to get the characteristic information of the vehicle, the maximum likelihood estimation algorithm is used to fuse the matching vehicle information. Hierarchical decision tree algorithm is used to classify the models. After the actual Lane field experimental test, the algorithm has better classification results compared with the results of a single sensor node models.

Copyright

© 2017, 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  - Xu LEI
AU  - Shuai WANG
AU  - Li-wang MA
AU  - Rong HE
PY  - 2016/12
DA  - 2016/12
TI  - Research on Vehicle Classification Algorithm Based on Information Fusion of Dual Reluctance Sensor
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 728
EP  - 733
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.101
DO  - 10.2991/cnct-16.2017.101
ID  - LEI2016/12
ER  -