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Knowledge based signal processing, waveform diversity and systems engineering

机译:基于知识的信号处理,波形分集和系统工程

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Future multi-mission / multi-mode sensors and radio frequency systems will have to operate in electromagnetically dense signal environments. Furthermore, a diverse cadre of concepts may result in a variety of fielded systems, all operating in close proximity to each other. While this ever growing collection of concepts bodes well for the rapid transition of new technology, these systems must share spectrum, measured data, and mission responsibility in order to perform in an economical and effective manor. Key to the successful fielding of these new sensors is knowledge based control of signal processing algorithms and radiated waveforms, as well as platform dynamics. In this tutorial, foundational technologies and their functional relationships will be presented in order to illustrate performance improvements relative to classical approaches. Knowledge Based STAP, Expert Systems CFAR, RF Tomography and Sensors as Robots technologies are presented as offering a way forward from a radar perspective.
机译:未来的多功能/多模式传感器和射频系统必须在电磁密集的信号环境中运行。此外,各种各样的概念可以导致各种威胁系统,所有这些系统都可以彼此靠近邻近操作。虽然这一概念概念的概念良好的概念,但这些系统必须共享频谱,测量数据和使命责任,以便在经济和有效的庄园中进行。这些新传感器的成功场所的关键是基于知识的信号处理算法和辐射波形的控制,以及平台动态。在本教程中,将提出基础技术及其功能关系,以说明相对于古典方法的性能改进。基于知识的STAP,专家系统CFAR,RF断层扫描和传感器作为机器人技术呈现为从雷达角度向前提供的方式。

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