Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

Track Data Split Measure Based on Least Square Algorithm

Authors
Xingfu Zhang, Yanbo Li, Lei Liu, Feng Zhao, Jingfeng Song
Corresponding Author
Xingfu Zhang
Available Online August 2016.
DOI
https://doi.org/10.2991/cset-16.2016.38How to use a DOI?
Keywords
Data splice, data fitting, least square algorithm
Abstract
This paper describes the split measure algorithm about tracks data. In accordance with the needs of the project, the algorithm can be used to measure the two parts, the three parts and the five parts of the data and then combine them together. The algorithm is based on the overlap area of each measurement. And the best stitching position can be obtained by using the least square method. Error analysis of two parts, three parts and five parts measure respectively is carried out. For several measure requirements, Error analysis shows that the theoretical maximum error of the algorithm is less than the actual requirements of the project. Finally, we selected 300 data from a sine wave, and using these 300 data to simulate the 3 meters long track. Then to simulate five measurements, and every two measurements overlap 0.5 meters. Experimental data analysis shows that the algorithm can indeed meet the requirements of practical application.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-213-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/cset-16.2016.38How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xingfu Zhang
AU  - Yanbo Li
AU  - Lei Liu
AU  - Feng Zhao
AU  - Jingfeng Song
PY  - 2016/08
DA  - 2016/08
TI  - Track Data Split Measure Based on Least Square Algorithm
PB  - Atlantis Press
SP  - 156
EP  - 158
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.38
DO  - https://doi.org/10.2991/cset-16.2016.38
ID  - Zhang2016/08
ER  -