Abstract
To realize space-sea integrated communication between multiple ships under the BeiDou-3 satellite communication topology, this paper proposes a new satellite-ship autonomous communication system based on a deep learning anomaly detection algorithm. A deep learning algorithm combining coarse-grained detection (piecewise oversampling principal component analysis, POsPCA) and fine-grained sorting (VAE and differential ARIMA joint model) was established to realize two-way autonomous communication between BeiDou-3 satellites and ships. Specifically, a segmented oversampling principal component analysis algorithm was proposed to analyze anomalous BeiDou-3 short message data segments in the coarse-grained detection stage. In the fine-grained sorting stage, a joint fusion reconstruction and prediction model was proposed to calculate the fine-grained anomaly score. The autonomous communication system establishes the communication priority of multiple ships based on this score. Using the experimental platform independently built for this study to verify the algorithm performance, the proposed algorithm effectively realizes the priority ranking of anomaly scores by detecting abnormal data segments, effectively saving 64.4% of BeiDou-3 satellite short message communication resources.










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Data availability
The data that support the findings of this study are openly available in the China International Maritime Administration official website-Navigation assistance integrated service system [Internet, not free]. Available from: https://ais.msa.gov.cn/. and reference number Ref [11].
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Acknowledgements
This work was supported by the National Natural Science Foundation of China subsidization project (51579047), the Natural Science Foundation of Heilongjiang Province (QC2017048), the Natural Science Foundation of Harbin (2016RAQXJ077), and the fundamental research funds for the central universities.
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Di Wu conceived the idea and designed the experiments with Sheng Liu. Wei Wei and Yu Sui wrote the main manuscript and reviewed the paper. All components of this research were carried out under the supervision of Sheng Liu.
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1. This paper proposes a new satellite-ship autonomous communication system with an integrated deep learning anomaly detection algorithm;.
2. An anomaly detection algorithm for coarse-grained detection, POsPCA, and fine-grained sorting (VAE and differential ARIMA joint model) is proposed;.
3. A real experimental platform for autonomous communication systems is built and effectively saves 64.4% of BeiDou satellite short message communication resources.
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Wu, D., Liu, S., Wei, W. et al. A new satellite-ship autonomous communication system with an integrated deep learning anomaly detection algorithm. Multimed Tools Appl 83, 74075–74100 (2024). https://doi.org/10.1007/s11042-024-18567-4
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DOI: https://doi.org/10.1007/s11042-024-18567-4