Metacognition for Radar Coexistence
Metacognition for Radar Coexistence
复制标题
雷达共存的元认知
DOI：
10.1109/radar42522.2020.9114775
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Baylis
中科院分区：
文献类型：
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作者：
Anthony Martone;K. Sherbondy;J. Kovarskiy;B. Kirk;C. Thornton;Jonathan Owen;Brandon Ravenscroft;Austin Egbert;Adam C. Goad;Angelique Dockendorf;R. M. Buehrer;Ram M. Narayanan;S. Blunt;C. Baylis
在本文中，我们研究了一个元认知雷达（MCR）模型，全面结合不同的认知雷达（CR）的战略，在拥挤的电磁环境（EME）的先进性能。该模型随着频谱环境和目标的变化而改变CR策略，以实现高效的雷达动态频谱访问（DSA）。该模型首先实现频谱感知，然后进行频谱分类，以识别已知的电磁辐射场景。这些频谱场景评估被动传感过程收集的时频数据的拥塞和复杂性。该评估优先考虑对给定光谱条件有效的可能CR策略。然后MCR模型通过学习评估不同的CR策略，并选择提供最佳雷达性能的技术。
In this paper we investigate a metacognitive radar (MCR) model that comprehensively combines disparate cognitive radar (CR) strategies for advanced performance in congested electromagnetic environments (EME). This model changes CR strategies as the spectral environment and target evolve for efficient radar dynamic spectrum access (DSA). The model first implements spectrum sensing followed by spectrum classification to identify known EME scenarios. These spectral scenarios assess the congestion and complexity of time-frequency data collected by the passive sensing process. This evaluation prioritizes possible CR strategies that are effective for the given spectral conditions. The MCR model then evaluates different CR strategies via learning and selects the technique that provides the best radar performance.