IEEE - Aerospace and Electronic Systems - December 2019 - 12

An Overview of Cognitive Radar: Past, Present, and Future
in more detail in Section V. As enabling technologies
mature, continued research will push forward the design
of next-generation radar systems with ever increasing
cognitive capabilities.

DEFINITION AND CLASSIFICATION OF COGNITIVE
RADAR SYSTEMS
The increasingly common use of the relatively new term
"cognitive radar" has resulted in some debate as to how a
cognitive radar actually differs from other terms used-
such as intelligent or smart radar, fully adaptive radar
(FAR), and cognitive FAR-if at all; and what the technical requirements for describing a radar as "cognitive" are.
Descriptions of cognitive radar that have been proffered
include the following:
1) "Cognitive radar (CR), which differs from traditional active radar as well as fore-active radar by
virtue of the following capability: The development
of rules of behavior in a self-organized manner
through a process called learning from experience
that results from continued interactions with the
environment." (Haykin et al. [55]).
2) "A system that is capable of sensing, learning, and
adapting to complex situations with performance
approaching or exceeding that achievable by a subject matter expert." (Guerci et al. [64]).
3) "While a fully adaptive radar may employ feedback
and use prior knowledge stored in memory, a cognitive radar predicts the consequences of actions, performs explicit decision-making, learns from the
environment, and uses memory to store the learned
knowledge." (Bell et al. [44]).
4) "Cognitive radar is a radar system that acquires
knowledge and understanding of its operating environment through online estimation, reasoning, and
learning or from databases comprising context information. Cognitive radar then exploits this acquired
knowledge and understanding to enhance information extraction, data processing, and radar management." (Charlish et al. [65] in 2017).
5) "A cognitive radar system follows the four principles of cognition: The perception-action cycle,
memory, attention, and intelligence." (Farina et al.
[66] in 2017).
As part of the work of the NATO SET-227 Task Group
on Cognitive Radar, active between 2015 and 2019, and of
which all the authors are a member, numerous discussions
on the characteristics of cognitive radar were conducted,
which highlighted the need for and insights on what a
definition might look like. In 2017, Dr. Chris Baker and
12

Dr. Hugh Griffiths spearheaded efforts to include a formal
definition of cognitive radar in the IEEE Standard Radar
Definitions 686 [67]: "A radar system that in some sense
displays intelligence, adapting its operation and its processing in response to a changing environment and target
scene. In comparison to adaptive radar, cognitive radar
learns to adapt operating parameters as well as processing
parameters and may do so over extended time periods."
From these definitions, it may be seen that there is
general agreement in the cognitive radar research community concerning some common elements that must be present in a radar system for it to be classified as cognitive.
These are the same characteristics identified by Fuster [5]
within the context of cognitive psychology: the PAC,
attention, and language. First, the PAC is the framework
which provides for closed-loop feedback in the radar
transceiver. If these functions were not to exist, then the
ability to adapt based on the system's perception of the
environment would be absent. Second, attention enables
the radar to focus its resources on critical aspects of the
observed scene. This is a characteristic that all multifunctional radars must possess as a resource management
requirement. Finally, language may be viewed as the ability to encode data such that it is possible for the system to
store, recall, and disseminate the information both internally and externally, but more broadly can embody any
set of rules for communicating information, including
internal messaging as part of decision making. As the storage and recall of information are essential to learning and
decision making, without it the system would arguable be
hindered from being responsive to any perception of outside stimuli.
The remaining two elements of Fuster's framework
for cognition includes memory and intelligence, and are
aspects regarding which designs begin to diverge. Here,
memory alludes not just to physical storage devices, which
could hold prior knowledge, but to memories of learned
experiences gained during the course of extended periods
of observations. Thus, memory can be said to function at
varying levels: 1) as a fixed internal knowledge base, 2) as
a dynamic knowledge base updated by an external source,
and 3) as an online learning capable system. Similarly,
intelligence may be characterized into varying degrees
based upon 1) the complexity of the decision-making
mechanism, and 2) capacity to plan long-term behavior.
Conceivably, a radar operating at a high level of cognition
would even be anticipative, planning based on its predictions of future outcomes.
Thus, an overarching classification scheme that would
allow flexibility in the definition and identification of cognitive characteristics as recently been proposed [68],
which relies on grading systems based on the degree of
planning sophistication (P), decision mechanism sophistication (D), and memory sophistication (M). This taxonomy is depicted by the 3-D synthetic classification Space,

IEEE A&E SYSTEMS MAGAZINE

DECEMBER 2019

IEEE - Aerospace and Electronic Systems - December 2019

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