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A Sorting Algorithm for Multiple Frequency-Hopping Signals in Complex Electromagnetic Environments

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Abstract

To improve the low accuracy of multiple frequency-hopping (multi-FH) signals sorting under complex electromagnetic environment and low signal-to-noise ratio, this paper proposes a multi-FH signals sorting algorithm based on the problems of mixed matrix time varying, existing collision FH signal and frequency switching time in the actual FH communication system. Firstly, received signals are converted to time–frequency domain by using short-time Fourier transform. After the time–frequency signals are subjected to adaptive noise reduction, the time–frequency information is extracted through the frequency clustering algorithm. Secondly, it combines the characteristics of the interference signals such as fixed-frequency signals and swept-frequency signals. Then, the direction of arrival (DOA) of each FH signal is estimated, and the multi-FH signals are initially sorted according to the frequency dwell time and DOA. Time of arrival algorithm based on center of gravity is adopted to sort specific source signals, which have same dwell time and DOA. Finally, the collision signal is located by scanning the switching time of each sorted FH signals, and the suitable class is found based on the estimated DOA and the hopping time. Ultimately, the frequencies of each source signal are sorted out. The experimental results show that the accuracy of multi-FH sorting is above 90% in the complex electromagnetic environment, which improves the low multi-FH sorting accuracy of actual FH system under the condition of existing synchronous and asynchronous FH signals.

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Acknowledgements

We gratefully acknowledge anonymous reviewers who read drafts and made many helpful suggestions. This work is supported by National Nature Science Foundation of China (61201134).

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Correspondence to Weihong Fu.

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Fu, W., Hu, Z. & Li, D. A Sorting Algorithm for Multiple Frequency-Hopping Signals in Complex Electromagnetic Environments. Circuits Syst Signal Process 39, 245–267 (2020). https://doi.org/10.1007/s00034-019-01160-8

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  • DOI: https://doi.org/10.1007/s00034-019-01160-8

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