Off Line Fingerprint Database Based on Multivariate Gaussian Mixture Model

Ningning QIN; Chao WANG; Le YANG; Shunyuan SUN

doi:10.11999/JEIT200226

Volume 43 Issue 6

Jun. 2021

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Ningning QIN, Chao WANG, Le YANG, Shunyuan SUN. Off Line Fingerprint Database Based on Multivariate Gaussian Mixture Model[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1772-1780. doi: 10.11999/JEIT200226

Citation:

Ningning QIN, Chao WANG, Le YANG, Shunyuan SUN. Off Line Fingerprint Database Based on Multivariate Gaussian Mixture Model[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1772-1780. doi: 10.11999/JEIT200226

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Off Line Fingerprint Database Based on Multivariate Gaussian Mixture Model

doi: 10.11999/JEIT200226 cstr: 32379.14.JEIT200226

1.
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
2.
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3.
Department of Electrical and Computer Engineering, University of Canterbury, Christchurch 8011, New Zealand

Funds: The National Natural Science Foundation of China (61702228, 61803183), The Natural Science Foundation of Jiangsu Province (BK20170198, BK20180591), The Open Fund of Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space of Ministry of Industry and Information Technology(KF20202104)

Received Date: 2020-03-31
Rev Recd Date: 2020-08-24

Available Online: 2020-09-03

Publish Date: 2021-06-18

Abstract

Abstract

For the fluctuation of single sampling measurement value and the mutual interference between signals in indoor environment, this paper proposes an indoor positioning system based on the partition MultiVariate Gaussian Mixture Model(MVGMM). According to the Access Point (AP) position and indoor spatial structure, the system uses SVM classification in “one-against-all” form to partition the target area in order to predict the subarea with signal changes. A MVGMM based on the mutual interference between signals is established by using the coupling relationship between multiple communication devices in the partition. It is important to improve the positioning accuracy which is affected by signal fluctuation. When the indoor environment changes, the adaptive updating algorithm based on the partition MVGMM can test the reliability of fingerprint data in each segmentation. Moreover, it can update the model parameters in the partition with large signal fluctuation by the adaptive algorithm to strengthen the coupling relationship between the model and the existing environment. Experimental result demonstrates that the proposed algorithm can build a stable and maintainable indoor signal distribution model by using a relatively small number of sample data. Its positioning accuracy is also improved to a certain extent compared to other algorithms.
- Indoor positioning,
- MultiVariate Gaussian Mixture Model(MVGMM),
- Subarea,
- Adaptive algorithm

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References(18)

References

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