Cognitive Radar Target Detection and Tracking With Multifunctional Reconfigurable Antennas

Abstract

In contrast to the feed-forward sensing chain employed by classical radar systems, cognitive radars use the perceived information about the environment to reconfigure their transmissions. While most of the efforts in the literature focus on software-based adaptations, this article proposes adaptive control of a radar hardware, multifunctional reconfigurable antennas (MRAs), for target detection and tracking within the cognitive radar framework. A parasitic layer based MRA has the capability of dynamically changing its EM characteristics (mode of operation), e.g., antenna beam pattern, polarization, center frequency, or a combination of thereof. This work focuses on beam pattern recognition using a general Bayesian cognitive radar framework for target detection and tracking. A cognitive radar controller is designed to select the modes of an actual MRA by minimizing the Cramer lower bound for direction of arrival estimation. Simulation results show that the cognitive reconfiguration of the MRA offers superior tracking performance compared to classical antenna systems with no adaptivity.