IEEE - Aerospace and Electronic Systems - July 2021 - 22

Machine Learning Support for Radar-Based Surveillance Systems
Figure 37.
Example for a situational Bayesian network to detect air refueling events.
Figure 38.
Time slices for the DBN to detect air refueling events.
DYNAMIC BAYESIAN NETWORKS
DBNs are nothing new-they were developed in the 90s, but
have been rediscovered in the recent years and are contributing
to today's AI applications [76], [77]. They are probabilistic
graphical models for reasoning with uncertainty over
time. Reasoning with uncertainty and limited resources
plays a major role in everyday life and in many technical
applications ofartificial intelligence [78], [79]. In certain situations,
optimal decisions must be made quickly although
the current knowledge is incomplete. Therefore, DBNs are
very useful to cover specific use cases with abstract situations.
The abstract situations can be broken down into individual
small tasks, which observe parameters from real-time
streams and detect defined events when the conditions are
fulfilled. So, in combination with the anomaly detection
based on tracks, special use cases in the maritime and airspace
environment can be enriched with DBNs to satisfy
customer needs. However, this requires intensive discussion
about the definition ofthe use cases.
Typical applications for DBNs range from classifying
objects to detecting a specific event and predicting it. Therefore,
use cases have to be defined split into separate tasks,
which are implemented in microservices and orchestrated in
22
DBNs. Use cases are, e.g., detection of illegal diving and
fishing and tank washing, spoofing, prediction of pirate
attack in the maritime domain [81], [82], and detection of
suspicious activities, e.g., nightly patrol flights, air refueling
and aircraft emergency or hijacking in the airspace domain.
The individual nodes in the DBN are random variables
that are linked with conditional dependencies by the edges.
An air refueling event, for example, can be triggered if the
probability value for this event has exceeded a defined
limit. Figure 37 shows an example for a DBN that detects
air refueling events as implemented in the AIRBUS product
Fortion SuRVIn ABI [80]. This value is calculated
from a Bayesian network, which changes dynamically over
time and also considers temporal relationships between different
nodes, see Figure 38.
CONCLUSION
In this article, many different methods were presented that
can be an asset to radar-based surveillance systems, but
without claiming completeness. Some techniques are in
the first phase or are about to be launched. Customers see
a great need to close skills gaps and are very interested in
IEEE A&E SYSTEMS MAGAZINE
JULY 2021

IEEE - Aerospace and Electronic Systems - July 2021

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