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.

中文翻译：

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使用多功能可重构天线进行认知雷达目标检测和跟踪

与经典雷达系统采用的前馈传感链相比，认知雷达使用感知到的环境信息来重新配置其传输。虽然文献中的大部分工作都集中在基于软件的适应上，但本文提出了雷达硬件、多功能可重构天线 (MRA) 的自适应控制，用于认知雷达框架内的目标检测和跟踪。基于寄生层的 MRA 具有动态改变其 EM 特性（操作模式）的能力，例如，天线波束图、极化、中心频率或其组合。这项工作的重点是使用通用贝叶斯认知雷达框架进行目标检测和跟踪的波束模式识别。认知雷达控制器旨在通过最小化到达方向估计的 Cramer 下界来选择实际 MRA 的模式。仿真结果表明，与没有自适应性的经典天线系统相比，MRA 的认知重构提供了卓越的跟踪性能。