A Comparative Study on different AI Techniques towards Performance Evaluation in RRM(Radar Resource Management)

S. MadhusudhanH, K. N. Sá
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引用次数: 1

Abstract

The multifunction radar (MFR) has to make a decision as to which functions are to be performed first or which must be degraded or even not done at all when there are not enough resources to be allocated. The process of making these decisions and determining their allocation as a function of time is known as Radar Resource Management (RRM). The RRM has two basic issues: task prioritization and task scheduling. The task prioritization is an important factor in the task scheduler. The other factor is the required scheduling time, which is decided by the environment, the target scenario and the performance requirements of radar functions. The required scheduling time could be improved by using advanced algorithm [1, 6].
雷达资源管理中不同人工智能技术性能评价的比较研究
当没有足够的资源分配时,多功能雷达(MFR)必须决定哪些功能应该首先执行,哪些功能必须降级甚至根本不执行。做出这些决策并确定其分配作为时间函数的过程被称为雷达资源管理(RRM)。RRM有两个基本问题:任务优先级和任务调度。任务优先级是任务调度程序中的一个重要因素。另一个因素是所需调度时间,这是由环境、目标场景和雷达功能的性能要求决定的。采用先进的算法可以提高所需的调度时间[1,6]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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