Abstract
Ship trajectory data contains a lot of valuable information, but abnormal data is inevitably generated in the process of data acquisition. A Temporal Convolutional Network (TCN) model-based method for detecting ship trajectory anomalies is proposed in this paper to avoid adverse effects on subsequent data analysis. First, a sliding time window algorithm is designed to extract the trajectory-subsequence because ship trajectories are time-series data. Then input the normal trajectory to train the TCN model. The model will learn the kinematic characteristics of the normal trajectory. When inputting an abnormal trajectory, the model reconstructs the anomalous trajectory and detects the anomaly by the reconstruction error. In addition, the multi-dimensional features of the trajectory are analyzed, and the identification of anomalies is based on the reconstruction error of each feature. With a recall of 93.51 and an F1 score of 0.913, the algorithm has significantly improved its performance in comparison with the LSTM model.
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Hao, J., Zhang, H. (2023). Ship Trajectory Anomaly Detection Based on TCN Model. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_341
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DOI: https://doi.org/10.1007/978-981-19-6613-2_341
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