Adaptive Recognition Method for Unknown Interference Based on Hilbert Signal Space

Guoce HUANG; Guisheng WANG; Qinghua REN; Shufu DONG; Weiting GAO; Shuai WEI

doi:10.11999/JEIT180891

Volume 41 Issue 8

Aug. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(8): 1916-1923

Guoce HUANG, Guisheng WANG, Qinghua REN, Shufu DONG, Weiting GAO, Shuai WEI. Adaptive Recognition Method for Unknown Interference Based on Hilbert Signal Space[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1916-1923. doi: 10.11999/JEIT180891

Citation:

Guoce HUANG, Guisheng WANG, Qinghua REN, Shufu DONG, Weiting GAO, Shuai WEI. Adaptive Recognition Method for Unknown Interference Based on Hilbert Signal Space[J]. Journal of Electronics & Information Technology, 2019, 41(8): 1916-1923. doi: 10.11999/JEIT180891

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Adaptive Recognition Method for Unknown Interference Based on Hilbert Signal Space

doi: 10.11999/JEIT180891

1.
College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2.
The 95910 Troop, Jiuquan 735000, China

Funds: The National Natural Science Foundation of China (61701521), The Postdoctoral Science Foundation of China (2016M603044), The Shaanxi Province Natural Science Foundation (2018JQ6074)

Received Date: 2018-09-18
Rev Recd Date: 2019-03-26

Available Online: 2019-04-23

Publish Date: 2019-08-01

Abstract

Abstract

In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%.
- Unmanned aerial vehicle communications,
- Unknown interference,
- Adaptive recognition,
- Hilbert signal space,
- Probabilistic Neural Network (PNN)

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References(21)

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