IEEE - Aerospace and Electronic Systems - October 2023 - 11

Soldi et al.
Figure 3.
Density maps of the maritime traffic in the Baltic Sea built using AIS data from 1 September to 15 September, 2022. A higher brightness corresponds
to a higher traffic density. Green and red dotted lines show the Nord Stream 1 and 2 gas pipelines, respectively. (a) Density map of
the entire maritime traffic of the Baltic Sea. (b) Density map of the stationary areas of the Baltic Sea.
used to derive a density map ofthe stationary areas, as shown
in Figure 3(b).
SEABED-TO-SPACE SITUATIONAL AWARENESS
The major challenge that operators and analysts face is
identifying patterns emerging within very large datasets,
e.g., AIS, SAR, optical, and MSP data, when the goal is to
anticipate possible future behaviors of suspicious assets
and the related threats. In this context, information
undoubtedly plays a crucial role, and AI opens up unprecedented
possibilities for surveillance systems to improve
MS, and in particular the resilience of CUIs. AI and IF
can easily process immense volumes of information, fused
from a variety of sources and generated from a very large
number of monitoring assets on a day-to-day basis, thus
enabling a potential future transition to an holistic perspective
of S3A. The learned knowledge therefrom can be
used as a valuable support to the cognitive processes (perception,
comprehension, and projection) of analysts and
operators to anticipate future behaviors and/or identify
threats and critical situations that might endanger CUIs. In
the following, we will provide an overview of the state-ofthe-art
Bayesian IF and MTT, anomaly detection, and
automatic reasoning techniques, that might enable S3A
and improve the monitoring of CUIs.
BAYESIAN IF AND MTT
The main objective of a multisensor MTT method is to
sequentially estimate the number of targets together with
their states, e.g., position, velocity, course, and heading, in
a particular maritime area, by fusing measurements from
OCTOBER 2023
multiple heterogeneous sensors. Each measurement is
either a noisy observation of a target's kinematics, shapes,
or other features, or a false alarm. In a Bayesian formulation,
the MTT method amounts to estimating at each time
(approximations of) the marginal posterior distributions of
the detected targets' states, using all the measurements
available up to the current time. MTT methods have to
deal with various challenges, for example, the heterogeneity
of the different information sources [8], [9], asynchronicity,
out-of-sequence measurements [41], latency, and
the measurement-origin uncertainty (MOU) [42], i.e., the
fact that it is unknown, which target (if any) generated
which measurement. Existing MTT algorithms can be
broadly classified as vector-type algorithms, such as the
joint probabilistic data association filter [43] and the multiple
hypothesis tracker [44], [45], and set-type algorithms,
such as the (cardinalized) probability hypothesis density
filter [46], [47] and multi-Bernoulli filters [48]. Vectortype
algorithms represent the multitarget states and measurements
by random vectors, whereas set-type algorithms
represent them by random finite sets. Algorithms of both
types have been developed and evaluated, and several limitations
have been noted [9]. First, the fusion of heterogeneous
information sources is not straightforward. Second,
they do not adapt to time-varying model parameters. And
third, their complexity usually does not scale well in relevant
system parameters, e.g., the number of sensors.
An emerging approach to MTT and IF-one with flexibility,
low complexity, and useful scalability-is based
on a factor graph and the sum-product algorithm
(SPA) [42]. First, a factor graph representing the statistical
model of the MTT problem is derived; then, the SPA is
used to solve efficiently the MOU problem and obtain a
principled and intuitive approximation of the Bayesian
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