IEEE - Aerospace and Electronic Systems - July 2021 - 20

Machine Learning Support for Radar-Based Surveillance Systems
Figure 31.
Different trajectory views of the labeled aircraft trajectories.
Figure 29.
Surveillance turns of the NATO AWACS fleet in 2018.
ACIVITY PATTERNS
Machine learning may also be applied to help detecting
aircraft maneuvers, which are of special interest [41],
[71]. Military aircraft in surveillance operation mode
often have a characteristic flight behavior, as shown in
Figure 29, which can be described as circulation of the
trajectory and can be distinguished from waiting loops
by their characteristic height. A circulation can be
detected with geometric invariants and counts of the
winding number. To identify turns with machine learning
a one-value feature was calculated from the sum of
heading changes within the sliding window of 5 min.
With a k-means clustering algorithm [45], [46], the
feature can be separated into the different flight behavior
classes: left turn, right turn, and straight flight. Finally,
with this generated label, a classifier for the real-time prediction
can be trained. See also Figure 30.
For the detection of start and landings, a similar
approach was used. A sliding window of 2 min was taken
and the difference between the first and last speed report
and altitude respectively, represented the feature. Again,
three clusters were assumed, which represent start, landing,
and straight flight [45], [46], see Figure 30. Figure 31
shows the resulted labeling on the trajectories. The results
can be used to identify airports, where labeled trajectories
converge. But it might also be of interest to detect the
beginning ofa landing procedure and to estimate the target
airport and time of arrival.
A combination of the different machine learning
approaches and a rule-based techniques, which is needed
to smooth the results with tuning and thresholding, was
implemented as a pattern recognition service, following
the Kappa architecture. Figure 32 shows the result for a
NATO AWACS trajectory including take off, landing,
and surveillance turn.
ANOMALY DETECTION
RULE-BASED APPROACH
Simple anomalies can already be detected with rule-based
approaches. Then, a scalable streaming service only needs
to observe a few parameters and recognize a significant
change or threshold transgression of them, e.g., sudden
Figure 30.
Results of the k-means clustering. Left: Sum of heading of the
sliding window versus random numbers (blue: straight flight).
Right: Altitude versus speed feature for take-off (red), straight
flight (green), and landing (blue).
20
Figure 32.
Different trajectory activities (blue: Surveillance turn; green:
take-off; red: landing).
IEEE A&E SYSTEMS MAGAZINE
JULY 2021

IEEE - Aerospace and Electronic Systems - July 2021

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - July 2021

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