Restoring Method of Vessel Track Based on AIS Information

Sang, Lingzhi; Yan, Xinping; Mao, Zhe; Ma, Feng

doi:10.1109/dcabes.2012.84

Cited by 10 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each full vessel trajectory is then divided into sub-trajectories, corresponding to when the ship stops for a time period longer than 3 h, e.g., when mooring, where only sub-trajectories with a minimum of 50 messages are used. The dynamic data are thereafter resampled and interpolated using the piece-wise cubic-spline interpolation as in [37], with a resampling time of 5 min. A filtering of the sub-trajectories is then made to remove erroneous data including nonphysical and unlikely values such as NAN values and messages with sog higher than 35 kn.…”

Section: Data Pre-processingmentioning

confidence: 99%

Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning

Sørensen

Heiselberg

2022

Sensors

View full text Add to dashboard Cite

Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of −9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios.

show abstract

Section: Data Pre-processingmentioning

confidence: 99%

Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning

Sørensen

Heiselberg

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Consequently, the center time T SAR is only a few seconds off on average. All AIS tracks were interpolated with a cubic spline as in [28,29] yielding a single AIS SAR coordinate at T SAR . The ship speed over ground (SOG) and course over ground (COG) are provided with the AIS messages, but as these are sometimes erroneous [3,4], we derived the SOG and COG directly from the interpolation.…”

Section: Ais-sar Data Temporal and Spatial Associationmentioning

confidence: 99%

SAR Ship–Iceberg Discrimination in Arctic Conditions Using Deep Learning

et al. 2022

View full text Add to dashboard Cite

Maritime surveillance of the Arctic region is of growing importance as shipping, fishing and tourism are increasing due to the sea ice retreat caused by global warming. Ships that do not identify themselves with a transponder system, so-called dark ships, pose a security risk. They can be detected by SAR satellites, which can monitor the vast Arctic region through clouds, day and night, with the caveat that the abundant icebergs in the Arctic cause false alarms. We collect and analyze 200 Sentinel-1 horizontally polarized SAR scenes from areas with high maritime traffic and from the Arctic region with a high density of icebergs. Ships and icebergs are detected using a continuous wavelet transform, which is optimized by correlating ships to known AIS positions. Globally, we are able to assign 72% of the AIS signals to a SAR ship and 32% of the SAR ships to an AIS signal. The ships are used to construct an annotated dataset of more than 9000 ships and ten times as many icebergs. The dataset is used for training several convolutional neural networks, and we propose a new network which achieves state of the art performance compared to previous ship–iceberg discrimination networks, reaching 93% validation accuracy. Furthermore, we collect a smaller test dataset consisting of 424 ships from 100 Arctic scenes which are correlated to AIS positions. This dataset constitutes an operational Arctic test scenario. We find these ships harder to classify with a lower test accuracy of 83%, because some of the ships sail near icebergs and ice floes, which confuses the classification algorithms.

show abstract

“…The observed trajectory data that consisted of the latitude, the longitude, the course, and the speed of the target vessel were interpolated at 1 s intervals by applying a cubic spline function. The performance of this interpolation method had proven in [30,31]. Then, the data reduction was performed in the light of moving speed of the ships, so that the intervals of the time stamps were changed from 1 s to 2 min.…”

Section: Preprocessing Ais Datamentioning

confidence: 99%

Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data

Park

Jeong

Park

2021

JMSE

View full text Add to dashboard Cite

According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship's trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship's trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.

show abstract

Restoring Method of Vessel Track Based on AIS Information

Cited by 10 publications

References 4 publications

Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning

Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning

SAR Ship–Iceberg Discrimination in Arctic Conditions Using Deep Learning

Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data

Contact Info

Product

Resources

About