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Language-Based Cost Functions for Fully Adaptive Radar Under Imprecise Performance Standards

机译：不精确性能标准下完全自适应雷达的基于语言的成本函数

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摘要

There is a push to create radar systems that optimize their own performance in real-time. In order to accomplish this, the performance standards of the radar system must be known. Often, this is given in imprecise, natural language statements describing the goals and requirements of the radar system by individuals in charge of the “big picture.” In this paper, we use a previously developed method called language-based cost functions (LBCFs) to optimize the performance of a fully adaptive radar target tracker when performance standards are given by vague statements in natural language. This is followed by a catch-all stochastic algorithm to find relaxed, time efficient solutions. The full method is then tested on a basic simulation of a human running. Results show that the combination of LBCFs and the optimization algorithm allow the radar to far outperform a classical non-adaptive radar. Results also show that the specific optimization algorithm used impacts the radar's ability to find satisfactory solutions which in turn dramatically alters the tracker's performance.
机译：有一种推动器创建雷达系统，实时优化自己的性能。为了实现这一点，必须知道雷达系统的性能标准。通常，这是不精确的，自然语言陈述，描述了负责“大图片”的雷达系统的目标和要求。在本文中，我们使用先前开发的方法称为基于语言的成本函数（LBCF），以优化在自然语言中的模糊语句给出性能标准时完全自适应雷达目标跟踪器的性能。这是捕获 - 所有随机算法，可以找到轻松，时间高效的解决方案。然后在人类运行的基本模拟上测试完整方法。结果表明，LBCFS和优化算法的组合允许雷达到远优惠的经典非自适应雷达。结果还表明，使用的特定优化算法影响了雷达找到令人满意的解决方案的能力，这又会大大改变跟踪器的性能。

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