Dynamic threshold based target signal cooperative extraction method for high frequency electromagnetic environment measurement

Shen, Xi; Ma, Yan; Yu, Junchu; Wang, Wenshuai; Jia, Haiqiang; Jun-feng, Cui; Cheng, Siyue

doi:10.1109/ccsse.2017.8087993

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Up till now, there are many researches for how to obtain signal threshold  [23][24][25]. In [23], an automatic algorithm extraction of the signal components (useful information) from the noisy mixture is based on the time-frequency distribution segment to a fixed number of data classes is proposed, which depends on the differences in the time frequency structures of noise and signal components.…”

Section: Aadaptive Threshold Calculation Methods Figure 5 Schematic Diagram Of Spectral Data Distributionmentioning

confidence: 99%

“…In [23], an automatic algorithm extraction of the signal components (useful information) from the noisy mixture is based on the time-frequency distribution segment to a fixed number of data classes is proposed, which depends on the differences in the time frequency structures of noise and signal components. In [24] paper innovatively proposed a dynamic threshold based target signal cooperative extraction method for high frequency electromagnetic environment measurement, which reduces the influence of manual factors and improves the automatic degree and efficiency of high frequency electromagnetic environment measurement, but it's not completely applicable to detect uav signals which is in a moving status and complex frequency environment.…”

Section: Aadaptive Threshold Calculation Methods Figure 5 Schematic Diagram Of Spectral Data Distributionmentioning

confidence: 99%

“…Time domain detection methods include image analysis, energy detection, correlation detection, etc. [23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

Ye¹,

Zou²,

Gao³

et al. 2021

IEEE Access

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The rapid development of civil UAV promotes the social and economic development, and the frequent "flying illegally" events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and control link has the advantages of anti-jamming and anti-interception, and its complex electromagnetic environment, which also brings great difficulties to UAV detection. In this paper, the detection of civil UAV is realized by frequency hopping signal monitoring. Firstly, by analyzing the signal characteristics of UAVs, an adaptive noise threshold calculation method is proposed for find the signals from spectrum data. Then, the improved clustering analysis algorithm is proposed based on constructed the waveform shape characteristics and peak characteristics of UAV frequency hopping signal. Finally, according to the designed experimental process, the experimental environment is set up, and the UAV monitoring, discovery and parameter estimation are realized by using the improved clustering analysis algorithm, and compared with K-means, K-means++, DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect.INDEX TERMS Civil UAV, Hopping signal detection, Clustering.

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Section: Aadaptive Threshold Calculation Methods Figure 5 Schematic Diagram Of Spectral Data Distributionmentioning

confidence: 99%

Section: Aadaptive Threshold Calculation Methods Figure 5 Schematic Diagram Of Spectral Data Distributionmentioning

confidence: 99%

See 1 more Smart Citation

A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

Ye¹,

Zou²,

Gao³

et al. 2021

IEEE Access

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“…The duration of the search process and the reliability of the received information depend on the completeness of solving tasks by search equipment. The completeness and speed of their implementation, the effectiveness of the search engine, the reliability of the information obtained and the probability of decision-making depend on the structure of the search engine and the characteristics of the tools used in it [6,8].…”

Section: Introductionmentioning

confidence: 99%

Methods for Assessment and Forecasting of Electromagnetic Radiation Levels in Urban Environments

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Lishchynovska

et al. 2021

IAPGOS

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Methods for synthesis a structural diagram of the processes for detecting and locating technical information leakage channels are analysed. Software for defining a controlled room zone was also presented. A proprietary approach to search the electromagnetic environment under radio interference has been developed to detect devices for unauthorized control of acoustic information using microphones and transmission of this information using a radio channel.

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DWAFE: Achieve Accurate AIOps Fault Early Warning

Yuan¹,

Gui²,

Wang³

et al. 2021

Communications in Computer and Information Science

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Dynamic threshold based target signal cooperative extraction method for high frequency electromagnetic environment measurement

Cited by 4 publications

References 8 publications

A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

Methods for Assessment and Forecasting of Electromagnetic Radiation Levels in Urban Environments

DWAFE: Achieve Accurate AIOps Fault Early Warning

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