IEEE - Aerospace and Electronic Systems - June 2021 - 21

Credit: Image licensed by Ingram Publishing
environmental models structured within suitable ontologies
[12] can be a useful source of hints on suitable inference
schemes for debris detection and tracking from a
radar perspective. On the other side, these cues may also
support a more confident exploitation of radar assets by
aerospace communities to nurture and validate catalogs of
debris populations. Finally, such a reasoning provides a
set of boundary conditions for framing the architectural
design of a novel archetype ofSBR to support SSA.
The remainder of this article is structured as follows.
The section " Debris-Class Populations " introduces the
environmental scenario with an overview of debris-classpopulations
currently catalogued by aerospace communities.
The section " Validation Methods of Debris Catalogs "
describes the methodologies adopted by aerospace communities
for validating such catalogs with relevant comments
on inferences from a radar perspective. The section
" Channel Phenomenology " addresses the channel phenomenology
where the spatial medium is possibly affected
by space weather with an influence on electromagnetic
propagation. The section " Target Phenomenology "
reviews the target phenomenology with a focus on the
expected scattering behavior of debris. The section
" Motion Models " discusses reasonable translational and
rotational motion models of debris. Finally, " Conclusions "
outlines conclusions and a way forward.
DEBRIS-CLASS POPULATIONS
Considering the volume of space around the Earth, the
cardinality of debris with an average diameter larger than
1 mm is estimated to exceed several hundred trillions
of items. Yet, when the average diameter is larger than
1 mm, the debris population size shrinks to roughly several
hundred millions of items. Nevertheless, when the
average diameter is larger than 1 cm, the debris population
reaches several hundred thousands of items. If the focus is
on debris larger than 10 cm, the debris population count
reduces to tens of thousands of objects (see [5] and [13]).
JUNE 2021
From such a huge population cardinality, it appears
useful to identify debris-related taxonomies. Thorough
categorizations of debris populations appear in [1], labeled
by either the lack or abundance ofknowledge to " associate
the root cause of the debris to a launch event. " Unidentified
objects represent the former class, whereas the latter
class is further structured into several features and origin.
Namely, payloads, e.g., satellites instruments; payload
mission related objects, e.g., astronaut tools; payload
fragmentation debris, e.g., items fragmented or released
during a specific event; payload debris, e.g., items fragmented
or released during an unknown event; rocket body,
e.g., released orbital stages belonging to a launcher; rocket
mission related objects, e.g., shrouds and engines; rocket
fragmentation debris, e.g., objects created in case of a
launch vehicle explosion; and rocket debris, e.g., objects
created from a rocket body during an unknown event.
Moreover, a plethora of fragmentation events are also pinpointed
as metadata related to the break-up cause such as:
accidental, aerodynamics, anomalous, collision, deliberate,
electrical, propulsion, and unknown. Items are also
grouped into two major populations: a large-object population
whose debris size is roughly larger than 1 cm, and a
small-object population whose debris size is roughly
larger than 1 mm, yet smaller than 1 cm. For example,
micrometeoroids, solid rocket motor dust, paint flakes,
and ejecta belong to the small-object population. Multilayer
insulation materials as well as launch- and missionrelated
objects including the items tracked in the two-lineelement
set of orbital items (publicly shared by the United
States Air Force Space Command) belong to the largeobject
population. Basically, explosion and collision fragments,
sodium-potassium droplets, and solid rocket motor
slag belong to both classes. So far, such taxonomies have
been wisely organized by aerospace communities and can,
in turn, highlight operative target scenarios needed for
proper radar detection and tracking. For example, Figure 1
shows a pictorial representation of an operative scenario
from an SBR (the red point) perspective made of
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