用联邦学习和轨迹压缩扩展船舶自动识别系统覆盖范围
Federated Learning and Trajectory Compression for Enhanced AIS Coverage
- 将船舶变为主动传感器，通过联邦学习协同分析数据
- 采用带宽受限的轨迹压缩算法，优先传输异常数据
- 适合关注海上态势感知与低带宽通信的应用场景
本文提出VesselEdge系统，利用联邦学习和带宽约束下的轨迹压缩技术，提升海事态势感知能力，扩展船舶自动识别系统（AIS）的覆盖范围。该系统将船舶转化为移动传感器，支持在低带宽连接下实现实时异常检测与高效数据传输。系统集成M3fed联邦学习模型与BWC-DR-A轨迹压缩算法，优先处理异常数据。初步实验结果表明，基于历史数据，VesselEdge能有效提升AIS覆盖范围与态势感知水平。
原文摘要 · Abstract (English)
This paper presents the VesselEdge system, which leverages federated learning and bandwidth-constrained trajectory compression to enhance maritime situational awareness by extending AIS coverage. VesselEdge transforms vessels into mobile sensors, enabling real-time anomaly detection and efficient data transmission over low-bandwidth connections. The system integrates the M3fed model for federated learning and the BWC-DR-A algorithm for trajectory compression, prioritizing anomalous data. Preliminary results demonstrate the effectiveness of VesselEdge in improving AIS coverage and situational awareness using historical data.
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