Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines

Xiaohui LI; Kun FANG; Tao FAN; Jiawen LIU; Siting LÜ

doi:10.11999/JEIT200725

Volume 43 Issue 9

Sep. 2021

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Xiaohui LI, Kun FANG, Tao FAN, Jiawen LIU, Siting LÜ. Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2601-2607. doi: 10.11999/JEIT200725

Citation:

Xiaohui LI, Kun FANG, Tao FAN, Jiawen LIU, Siting LÜ. Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2601-2607. doi: 10.11999/JEIT200725

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Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines

doi: 10.11999/JEIT200725 cstr: 32379.14.JEIT200725

State Key Laboratory of ISN, Xidian University, Xi’an 710077, China

Received Date: 2020-08-14
Rev Recd Date: 2021-07-02

Available Online: 2021-07-15

Publish Date: 2021-09-16

Abstract

Abstract

In order to solve the problem that it is difficult to extract the Unmanned Aerial Vehicle (UAV) positioning signal from the environment with severe multipath interference in the passive positioning of the UAV, a UAV positioning signal separation based on Support Vector Machines (SVM) algorithm is proposed. During the training of the SVM model, the information entropy is obtained by calculating the Euclidean distance between the adjacent data sets of the UAV, and the model data is provided for the SVM to map the high-dimensional space. On this basis, the soft boundary of the threshold of the mapping function is added to make the model have the ability to adjust parameters adaptively to adapt to the data difference caused by the flexible movement of the UAV. Finally, an observer operating characteristic curve is constructed to obtain the result of UAV positioning signal separation. The simulation results show that the proposed algorithm can effectively separate the UAV positioning signal and noise.
- Unmanned Aerial Vehicle (UAV) positioning,
- Support Vector Machines (SVM),
- Information entropy,
- Noise separation

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References(18)

References

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