Language-Based Cost Functions: Another Step Toward a Truly Cognitive Radar

In order for a closed-loop radar system to adapt properly to changing operational conditions, the performance standards and requirements of the radar system must be known. Often, these performance standards are contained in statements made in language, which describe the different goals of the radar system. In this article, we explore the use of language-based cost functions (LBCFs) to encode performance standards made in imprecise language into an objective function, which a fully adaptive radar (FAR) can use to optimize its performance in real time. To enable the LBCFs, we also propose a statement decomposer, which takes in statements as inputs and yields the information required by LBCFs as outputs. A simulation study of a FAR tracking a running human is conducted with four different cost function methodologies: quadratic, weighted global criterion, fuzzy optimization, and LBCFs. Results show that when statements are properly formulated and presented, the statement decomposer is able to successfully extract the relevant information. Results also show that when paired with a previously developed optimization routine, the LBCF system is capable of autonomously creating cost functions that outperform the other cost functions in the simulated FAR target tracking scenario.