Authors:
(1) Ruochu Yang;
(2) Chad Lembke;
(3) Fumin Zhang;
(4) Catherine Edwards.
Table of Links
IV. CONCLUSION
In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a
data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms.
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This paper is