Cognitive Dynamic Systems: A Technical Review of Cognitive Radar

In this report we discuss the mechanisms of cognitive radar. This report is organized as follows: We start with the history of cognitive radar, where origins of the PAC, Fuster?s 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 (PAC) 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 (CKF), Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) 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. In addition, we suggest using the concept of risk management to better control parameters and improve performance in cognitive radar, especially in noise reduction. We believe, spectrum sensing can be seen as a potential interest to be used in cognitive radar and we propose a new approach Probabilistic Independent Component Analysis (PICA) which will presumably reduce noise based on estimation error in cognitive radar. Parallel computing is a concept based on divide and conquers mechanism, and in this report we suggest to use the parallel computing approach in cognitive radar by doing complicated calculations or tasks to reduce processing time, increase speed of system and higher precision. With the increased use of parallel computing in other fields of engineering, General-purpose computing on graphics processing units (GPGPU) would be the best way to implement parallel computing in cognitive radar.