  We start with the history of cognitive radar, where origins of the PAC,
Fuster research on cognition and principals of cognition are provided. Fuster
describes five cognitive functions: perception, memory, attention, language,
and intelligence. We describe the Perception-Action Cyclec as it applies to
cognitive radar, and then discuss long-term memory, memory storage, memory
retrieval and working memory. A comparison between memory in human cognition
and cognitive radar is given as well. Attention is another function described
by Fuster, and we have given the comparison of attention in human cognition and
cognitive radar. We talk about the four functional blocks from the PAC:
Bayesian filter, feedback information, dynamic programming and state-space
model for the radar environment. Then, to show that the PAC improves the
tracking accuracy of Cognitive Radar over Traditional Active Radar, we have
provided simulation results. In the simulation, three nonlinear filters:
Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter are
compared. Based on the results, radars implemented with CKF perform better than
the radars implemented with UKF or radars implemented with EKF. Further, radar
with EKF has the worst accuracy and has the biggest computation load because of
derivation and evaluation of Jacobian matrices. We suggest using the concept of
risk management to better control parameters and improve performance in
cognitive radar. We believe, spectrum sensing can be seen as a potential
interest to be used in cognitive radar and we propose a new approach
Probabilistic ICA which will presumably reduce noise based on estimation error
in cognitive radar. Parallel computing is a concept based on divide and
conquers mechanism, and we suggest using the parallel computing approach in
cognitive radar by doing complicated calculations or tasks to reduce processing
time.

Artificial Intelligence