COGNITION IS THE KEY TO THE NEXT GENERATION OF RADAR SYSTEMS

Motivated by the echo-location system of a bat, I described the idea of cognitive radar in 2006 for the first time. This paper expands on this novel idea. In particular, the paper focuses on a cognitive tracking radar, the implementation of which comprises two distinct functional blocks, one in the receiver and the other in transmitter with a feedback link from the receiver to the transmitter. To sense the radar environment, the receiver uses approximate Bayesian filtering, which is closely realized by a new nonlinear sequential state estimator, named the Cubature Kalman filter. To control the radar illumination, the transmitter uses an incremental dynamic programming, known as the $Q$ learning algorithm.