General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets
This work addresses the critical issue of unpredictable events causing abnormal behavior or loss of underwater gliders in oceanography, though it appears incremental as it applies existing anomaly detection methods to new data.
The paper tackles the problem of detecting anomalies like shark strikes or remora attachments in underwater gliders using an anomaly detection algorithm, validated on large-scale deployment datasets from real-world ocean environments, with results including prompt alerts for glider pilots to prevent harm.
Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the real-world ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to multiple datasets collected in real glider deployments led by the University of Georgia's Skidaway Institute of Oceanography (SkIO) and the University of South Florida (USF). In order to demonstrate the algorithm generality, the experimental evaluation is applied to four glider deployment datasets, each highlighting various anomalies happening in different scenes. Specifically, we utilize high resolution datasets only available post-recovery to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we simulate the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time.