Academic Journal
Frequency hopping signal detection based on optimized generalized S transform and ResNet
| Title: | Frequency hopping signal detection based on optimized generalized S transform and ResNet |
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| Authors: | Chun Li, Ying Chen, Zhijin Zhao |
| Source: | Mathematical Biosciences and Engineering, Vol 20, Iss 7, Pp 12843-12863 (2023) |
| Publisher Information: | AIMS Press, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Biotechnology LCC:Mathematics |
| Subject Terms: | frequency hopping signal detection, generalized s transform, genetic algorithm, convolutional neural network, Biotechnology, TP248.13-248.65, Mathematics, QA1-939 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1551-0018 |
| Relation: | https://doaj.org/toc/1551-0018 |
| DOI: | 10.3934/mbe.2023573?viewType=HTML |
| DOI: | 10.3934/mbe.2023573 |
| Access URL: | https://doaj.org/article/4f74df450ad94ced9d81215039d93fef |
| Accession Number: | edsdoj.4f74df450ad94ced9d81215039d93fef |
| Database: | Directory of Open Access Journals |
| ISSN: | 15510018 |
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| DOI: | 10.3934/mbe.2023573?viewType=HTML |
| Published in: | Mathematical Biosciences and Engineering |
| Language: | English |