Maritime Situation Awareness - NATO

Felix Opitz, Camilla Mohrdieck, Kaeye Dästner

Maritime Situation Awareness through Data Analytics, Machine Learning and Risk Assessment Based on Ship Trajectories

1st Maritime Situational Awareness Workshop MSAW 2019

Villa Marigola, Lerici (La Spezia), Italy, 8–10 October 2019

Content

8 October 2019 Maritime Situation Awareness through Data Analytics, Machine Learning and Risk Assessment Based on Ship Trajectories 2

• Introduction • Sources of Trajectories • Processing Architectures for Trajectories

• Unsupervised Machine Learning for Traffic • Area of Interest Extraction by Point Clustering • Pattern of Life Analysis

• Supervised Machine Learning for Classification • Heat Map Generation and Visual Situation Assessment • Conclusion

Sources of Trajectories Automatic Identification

System Automatic Dependent

Surveillance - Broadcast

worldwide, ~ 2,5 GB per day ~160K trajectories

worldwide, ~ 11 GB per day ~ 200K trajectories

identifier: MMSI identifier: ICAO ASTERIX CAT 21 ITU-R M.1371

Maritime/Ground Moving Target Indication Radar

e.g. STANAG 4607 identifier: track number

wide area ~ 10K trajectories

P = (p1, … , pn) Trajectory = Chronologically ordered sequence of positions pi. Position: e.g. latitude, longitude and altitude.


Processing Architectures for Trajectories / λ-Architecture

• Continuous reception and storage of raw data • Cleaning, transformation and presentation s.t.

applications can access needed information quickly • Data mining

• Data analytics, statistics, heat maps • Training of supervised/unsupervised ML, e.g.

NN, RNN, CNN, Autoencoders, Random Forest, … • Generation of classifiers or predictors • Pattern of life, Activity based Intelligence

• Non-cooperative targets: Tracking • Real time aspects

• Application of learned patterns and classifiers • Anomaly detection, predictive estimations,

classification or even identification


1 February, 2017

5

C++ Java kafka

cassandra

hadoop

scikit learn Spark TensorFlow

SparkMLib Keras

nifi

OrientDB

cassandra OrientDB

kafka

kafka

Hive

Python Scala Java Processing Architectures for Trajectories

Unsupervised Machine Learning for Traffic Area of Interest Extraction by Point Clustering

• Areas/Points of interest: e.g. airports, heliports, harbors, offshore drilling rigs and wind parks or landing areas of ferries

• Cluster Algorithms: DBSCAN, OPITCS, HDBScan… / Convex Hull calculation • Challenges: e.g. unknown number of clusters / distributed processing


Unsupervised Machine Learning for Traffic Area of Interest Extraction by Point Clustering Segmentation of trajectories Ports / Airports and Statistics

• Additional insights: • Relationships between clusters (e.g. transfers) • Statistics about areas of interest (e.g. logistics)


Unsupervised Machine Learning for Traffic - Pattern of Life Analysis with HDBSCAN with Adaptive Scaling


Activity Based Intelligence based on Route Extraction: • Cluster Routes • Assign Trajectories to Routes • Calculate Route Probability • Calculate Route Variation AIS

ADS-B

Activity Based Intelligence based on Route Extraction: • Distance between Routes • Adaptive Scaling • Cluster Trajectories into Routes • Represent Routes by Averaged

Trajectory • Calculate Route Probability • Calculate Route Variation

FRA

ZRH

Rodby

Puttgarden

Unsupervised Machine Learning for Traffic - Pattern of Life Analysis Big Data


Unsupervised Machine Learning for Traffic – Pattern of Life Analysis / Activity Based Intelligence • Midterm route prediction

Established midterm prediction model for target routes based on historic data • Realtime anomaly detection / AIS/ADS-B spoofing

Derivations from the learned routes may be indications for anomalies / spoofing. • Support of realistic simulation

Also, the extracted routes may be used to simulate complex multi target scenarios taking into account historic data

• Activity based tracking The extracted routes can be used to improve target tracking based on more appropriate a priori data (similar to GMTI based airborne ground surveillance).

• Identification features Feature derived from route extraction are used for Identification based on IDCP

• Identify temporary changes of routes • Classification of / with routes


Supervised Machine Learning: AIS - fishing / non fishing Classification based on long track segments

learned predicted

Engaged in fishing

• Use complete trajectory segments

• Use AIS data to train fishing classifier

• Features: • Start position • End position • Mean speed • Total curvature • …


Supervised Machine Learning Ship classifier based on ship trajectories

Classification of Ship Types • Cargo • Fishing • Passenger • Tanker • Other based on • Random Forests and • DL CNN


Heat Map Generation and Visual Situation Assessment

• Grid based statistics • Frequency (Hits) • Kinematics: Speed, Course • Navigation Status, Target type, Military /

Civil, … • Uni-/multi-variate • Mono-/bi-/multi-modal • Anomaly detection through

• Graphical Layers • Automatic

• Requires Framework for Distributed Processing

• Also used within other ML assets


Heat Map Generation and Visual Situation Assessment Polar Operational Limitations Assessment Risk Indexing System (POLARIS)*

RVi : Risk Value (score) of ice type i Ci : Concentration of ice type i

nnRVCRVCRVCRIO +++= ...2211

* Joint German-Canadian Study led by Airbus DS in cooperation with Fraunhofer FKIE and Dalhousie University Ref.: Stoddard, M.A., Etienne, L., Pelot, R., Fournier, M. and Beveridge, L. “From Sensing to Sense-Making: assessing and visualizing ship operational limitations in the Canadian Arctic using open-access ice data”, in Sustainable Shipping in a Changing Arctic, eds: Hildebrand, L., Brigham, L., Johansson, T. Springer, pp.99-113, 2018. Stoddard, M.A., Etienne, L., Fournier, M., Pelot, R., & Beveridge, L. “Making sense of Arctic maritime traffic using the Polar Operational Limits Assessment Risk Indexing System (POLARIS)”, IOP Conf. Ser.: Earth Environ. Sci. 34 012034, pp.9, 2016.

• Proposed risk assessment framework for determining ship operational limitations in ice

• Produced by the International Association of Classification Societies (IACS)

• POLARIS provides a basic calculation to assess ship limitations based on a Risk Index Outcome (RIO):


Heat Map Generation and Visual Situation Assessment RIO Visualizations Varying Ship Class

• Seasonal Risk & Recommendations for ships with varying ice classes

Varying Statistical Aggregation • Best/ intermediate and worst case scenarios

for ship with particular ice class


Median Polaris Index - IA vessel – "Track Classification"

Heat Map Generation and Visual Situation Assessment Risk Evaluation and Trajectory Planning Trajectory Risk • Evaluate risk grid for

area and time of planned trajectory and for ice class of vessel

• map planned trajectory to risk grid

• Identify recommen-dations for trajectory segments

Trajectory Planning • Evaluate evolution of

tajectory risk over time • Identify safe travel times

for ship 8 October 2019 Maritime Situation Awareness through Data Analytics, Machine Learning and Risk Assessment Based on Ship Trajectories 16

Conclusion

Maritime Surveillance • takes place in ambiguous and complex environments with manifold threats. • Data driven methods result in big data problems:

Control large data volumes, High data turnover rates and Decisions making. Modern surveillance systems have to integrate big data concepts.

• Innovative big data and machine learning methods offer a chance to cope with existing and new challenges efficiently and effectively.

• Globally and persistently available position data received by networked AIS and ADS-B transceivers and sensors deliver multitudes of trajectories. This paves the way for advanced data analytics and supervised and unsupervised machine learning.

We show how data-driven approaches can add to maritime situation awareness and decision support by applying these technologies to selected use cases.


The reproduction, distribution and utilization of this document as w ell as the communication of its contents to others w ithout express authorization is prohibited. Offenders will be held liable for the payment of damages. All rights reserved in the event of the grant of a patent, utility model or design.

Dr. Felix Opitz Diplom-Mathematiker

Senior Expert Information Fusion

Ground & Coastal Protection Solutions, TECIC2 Communication, Intelligence & Security

T: +49 (0) 731.3798-2874 Airbus Defence and Space GmbH E: [email protected] Wörthstr. 85 89077 Ulm Germany

Thank you


Dr. Camilla Mohrdieck Diplom-Physikerin

Senior Expert Multi-Source Integration

Net Centric Solutions, TECIC6 Communication, Intelligence & Security


Kaeye Dästner Diplom-Physiker

Expert Sensor Grid Management

Ground & Coastal Protection Solutions, TECIC2 Communication, Intelligence & Security


Maritime Situation Awareness - NATO

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