IEEE - Aerospace and Electronic Systems - April 2022 - 23

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surveillance performance of a radar. In [12], the authors
propose a new methodology for multifunction radar performance
assessment for search-and-track missions.
The publications mentioned above consider single
radars, networks of ground-based or naval radars.
These radars are either static or their location needs to
be optimized such that they cover an area as large as
possible.
As opposed to the aforementioned publications, here
we focus on airborne radars. In the scenarios considered
here, the trajectory is controlled online by a pilot, who
also commands which volumes need to be searched for
objects of interest. Moreover, it is rather common that aircraft
fly in formations of two or more. Therefore, it is of
interest to consider the coordination ofsearch and tracking
efforts. By doing so sensor resources are not wasted by
executing the same tasks that another sensor already executes
or by assigning tasks to a platform with less favorable
geometry to execute them.
Similar problems have already been considered in the
robotics community and particularly in the unmanned
aerial vehicle (UAV) community. Therein it is usually of
interest to coordinate the efforts of several UAVs such
that ground enitites can be detected, tracked, and classified.
Typical problem formulations include the trajectory
optimization as well as the control of sensors, such as
radar and camera, see [13]-[15].
Here, we will focus on the control oftwo AESA radars,
mounted on an airborne platforms, using two cooperating
platform as use case example [Our concept is generic and
can be scaled to an arbitrary number of sensors and platforms].
When a mission begins radar resources are mainly
spent on search in order to detect objects of interest. As
time evolves and aircraft are detected, resources are shifted
from search to tracking tasks. Search and tracking tasks
compete for the limited radar resources, and therefore, an
appropriate resource tradeoff must be achieved, [16,
Ch. 5]: if too much resource is spent on search tasks, then
the quality of tracking tasks might be unacceptable or
tracks might be lost; ifon the other hand too much resource
is spent on tracking tasks, then new objects might not be
detected at all or only later than desired.
Our goal is to control multiple platforms' AESA
radars such that the joint search task along with the resulting
tracking tasks are executed cooperatively by two platforms
in a fashion, which optimizes the use of available
multiplatform radar resources. This is a first step toward
holistic multisensor multiplatform management. The latter
is envisaged to be necessary in dense scenarios, platforms
would get saturated when executing all tasks individually
on their own. If on the other hand the platforms share the
tasks to be executed, then all tasks might be executed,
potentially also more satisfactorily w.r.t. achieved performance
and quality.
USE CASE: COMBAT AIR PATROL (CAP)
Figure 1.
Illustration of air volume search by two aircraft. A volume can be
defined w.r.t. an aircraft or a stationary point on the ground.
APRIL 2022
Let us consider two airborne platforms, AC1 and AC2,
that are tasked with a CAP mission. During CAP they
must search a 3-D air volume, also called fighter area of
responsibility (FAOR), for other aircraft and track them.
In order to demonstrate the benefits arising from the use
of advanced sensor models and sensor management algorithms
that support sensor coordination, let us consider the
scenario depicted in Figure 2.
The FAOR to be covered is approximately 90 km
away from own ac and has the following dimensions: 40
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IEEE - Aerospace and Electronic Systems - April 2022

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