Outlier Detection of Light Buoy Telemetry and Telecontrol Data Based on Improved Adaptive ε Neighborhood DBSCAN Clustering

Abstract

In this paper, according to the water area of light buoy, the migration rule of light buoy in main channel is counted, and the frequency of light buoy passing through a certain position point in the process of migration is calculated, and the model is verified by buoy position data. An anomaly detection algorithm based on improved adaptive DBSCAN clustering is designed. The size of the ε neighborhood is adaptive according to the wind speed, wave height, and drift distance span of the water area where the light buoy is located. The experimental results show that the improved adaptive DBSCAN clustering algorithm can solve the problem that the common DBSCAN clustering algorithm takes the “hot” water area of the light buoy position or the most likely area in the light buoy migration process as the noise point.