Anomaly perception model of marine targets based on big data

Fanchao Meng; Lei Cao; Shengwen Cao; Xiaoyi Jiang; Wenhu Lu; Enhui Song; Chongjing Lv

doi:10.1117/12.3020980

23 January 2024 Anomaly perception model of marine targets based on big data

Fanchao Meng, Lei Cao, Shengwen Cao, Xiaoyi Jiang, Wenhu Lu, Enhui Song, Chongjing Lv

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129781B (2024) https://doi.org/10.1117/12.3020980
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Aiming at the dynamic perception problem of abnormal marine targets, this paper studies the abnormal perception model of marine targets based on big data. Firstly, the classification status of abnormal behaviors of marine targets is analyzed, and a three-way classification method of "identity-status-behavior" is proposed for marine targets. Secondly, the characteristics of abnormal identity, abnormal status and abnormal behavior of marine targets are studied. Based on the knowledge-driven method, the abnormal discrimination rules of marine targets are defined, and the rule-based anomaly detection algorithm of marine targets is designed. Finally, the anomaly perception model of marine targets based on the Flink is constructed to realize the identification of marine targets with abnormal identity, status and behavior. The model test was carried out using the real-time data of radar and AIS. The results show that, under the premise of stable and continuous data, the constructed model can quickly identify abnormal targets conforming to the predefined rules, support the real-time calculation of big data and parameter configuration, and have good real-time performance and flexibility.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fanchao Meng, Lei Cao, Shengwen Cao, Xiaoyi Jiang, Wenhu Lu, Enhui Song, and Chongjing Lv "Anomaly perception model of marine targets based on big data", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129781B (23 January 2024); https://doi.org/10.1117/12.3020980

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