Establishment Algorithm of Two Dimensional Fingerprint Database for Mine Workers Based on SVR-Kriging Interpolation

WANG Hongjun; ZHOU Yu; WANG Lunwen

doi:10.11999/JEIT170058

Volume 39 Issue 11

Nov. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(11): 2571-2578

WANG Hongjun, ZHOU Yu, WANG Lunwen. Establishment Algorithm of Two Dimensional Fingerprint Database for Mine Workers Based on SVR-Kriging Interpolation[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2571-2578. doi: 10.11999/JEIT170058

Citation:

WANG Hongjun, ZHOU Yu, WANG Lunwen. Establishment Algorithm of Two Dimensional Fingerprint Database for Mine Workers Based on SVR-Kriging Interpolation[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2571-2578. doi: 10.11999/JEIT170058

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Establishment Algorithm of Two Dimensional Fingerprint Database for Mine Workers Based on SVR-Kriging Interpolation

doi: 10.11999/JEIT170058

Funds:

The National Natural Science Foundation of China (61273302)

Received Date: 2017-01-16
Rev Recd Date: 2017-04-12
Publish Date: 2017-11-19

Abstract

Abstract

In order to overcome the limitation of one-dimensional model in accuracy of mine workers fingerprint location, a two-dimensional fingerprint location database algorithm for mine workers is proposed. The problem of the large data acquisition workload brought by the two-dimensional model is also solved by SVR-Kriging interpolation. Firstly, Gaussian filtering is used to preprocess the fingerprint information of the collected sampling point and the variation function is fitted by the Support Vector Regression (SVR). Then, the Kriging interpolation is used to complete the position fingerprint information of the un-sampled area in the two-dimensional meshing. Finally, the fingerprint location database of the mine workers is established by integrating the location fingerprint information of the sampling points and the interpolation points, laying the foundation for the follow-up mine workers fingerprint location. The simulation results show that the proposed algorithm can reduce the workload of data acquisition while ensuring the feasibility and the effectiveness of the algorithm and can guarantee high accuracy when positioning is performed through the location fingerprint.
- Mine positioning,
- Gauss filter,
- Variogram,
- Support Vector Regression (SVR),
- Kriging interpolation

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