Frequency hopping signal detection based on optimized generalized S transform and ResNet

Chun Li; Ying Chen; Zhijin Zhao; Chun Li; Ying Chen; Zhijin Zhao

doi:10.3934/mbe.2023573

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 12843-12863. doi: 10.3934/mbe.2023573

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Research article Special Issues

Frequency hopping signal detection based on optimized generalized S transform and ResNet

School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Academic Editor: Danilo Pelusi

Received: 09 March 2023 Revised: 11 May 2023 Accepted: 22 May 2023 Published: 02 June 2023

The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detection techniques have a high level of complexity. Therefore, this paper proposes a residual network and the optimized generalized S transform to detect frequency hopping signals. First, based on the time-frequency aggregation measure, the generalized S transform parameters $ \lambda $ and $ p $ are optimized using a multi-population genetic algorithm. Second, the optimized generalized S transform is used to determine a signal's time-frequency spectrum, which is then normalized to make this robust to noise power uncertainty. Finally, a residual network structure is designed which receives the time-frequency spectrum. To detect frequency hopping signals, the network automatically learns the time-frequency properties of signals and noise. Simulated findings indicate that the multi-population genetic algorithm not only increases optimization efficiency when compared to a regular genetic algorithm, but also has faster convergence and more stable optimization results. Compared with a hybrid convolutional network/recurrent neural network algorithm, the proposed technique is better at detection and has less computational and storage complexity.
- frequency hopping signal detection,
- generalized S transform,
- genetic algorithm,
- convolutional neural network
Citation: Chun Li, Ying Chen, Zhijin Zhao. Frequency hopping signal detection based on optimized generalized S transform and ResNet[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12843-12863. doi: 10.3934/mbe.2023573

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Abstract

The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detection techniques have a high level of complexity. Therefore, this paper proposes a residual network and the optimized generalized S transform to detect frequency hopping signals. First, based on the time-frequency aggregation measure, the generalized S transform parameters $ \lambda $ and $ p $ are optimized using a multi-population genetic algorithm. Second, the optimized generalized S transform is used to determine a signal's time-frequency spectrum, which is then normalized to make this robust to noise power uncertainty. Finally, a residual network structure is designed which receives the time-frequency spectrum. To detect frequency hopping signals, the network automatically learns the time-frequency properties of signals and noise. Simulated findings indicate that the multi-population genetic algorithm not only increases optimization efficiency when compared to a regular genetic algorithm, but also has faster convergence and more stable optimization results. Compared with a hybrid convolutional network/recurrent neural network algorithm, the proposed technique is better at detection and has less computational and storage complexity.

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