EFE-LSTM: A Feature Extension, Fusion and Extraction Approach Using Long Short-Term Memory for Navigation Aids State Recognition

Jingjing Cao, Zhipeng Wen, Liang Huang, Jinshan Dai () and Hu Qin
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Jingjing Cao: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Zhipeng Wen: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Liang Huang: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
Jinshan Dai: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Hu Qin: School of Management, Huazhong University of Science and Technology, Wuhan 430063, China

Mathematics, 2024, vol. 12, issue 7, 1-20

Abstract: Navigation aids play a crucial role in guiding ship navigation and marking safe water areas. Therefore, ensuring the accurate and efficient recognition of a navigation aid’s state is critical for maritime safety. To address the issue of sparse features in navigation aid data, this paper proposes an approach that involves three distinct processes: the extension of rank entropy space, the fusion of multi-domain features, and the extraction of hidden features (EFE). Based on these processes, this paper introduces a new LSTM model termed EFE-LSTM. Specifically, in the feature extension module, we introduce a rank entropy operator for space extension. This method effectively captures uncertainty in data distribution and the interrelationships among features. The feature fusion module introduces new features in the time domain, frequency domain, and time–frequency domain, capturing the dynamic features of signals across multiple dimensions. Finally, in the feature extraction module, we employ the BiLSTM model to capture the hidden abstract features of navigational signals, enabling the model to more effectively differentiate between various navigation aids states. Extensive experimental results on four real-world navigation aid datasets indicate that the proposed model outperforms other benchmark algorithms, achieving the highest accuracy among all state recognition models at 92.32%.

Keywords: navigation aids; state recognition; extended space; multi-domain features; hidden feature extraction; maritime safety (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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