Artificial intelligence meets radar resource management: A comprehensive background and literature review

Abstract

A multi‐function radar is designed to perform disparate functions, such as surveillance, tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) budget. A radar resource management (RRM) module within a radar system makes decisions on prioritisation, parameter selection, and scheduling of associated tasks. However, optimal RRM algorithms are generally computationally complex and operational radars resort to heuristics. On the other hand, algorithms based on artificial intelligence (AI) have been shown to yield near‐optimal radar resource allocation results at manageable computational complexity. This survey study aims at enabling researchers and practitioners better understand the application of AI in RRM‐related problems by providing a thorough literature review of AI‐based RRM techniques. We first provide background concepts in RRM followed by a brief review of Symbolic‐AI techniques for RRM. We mainly focus on the applications of state‐of‐the‐art machine learning techniques to RRM. We emphasise on the recent findings and their potential within practical RRM scenarios for real‐time resource allocation optimisation. The study concludes with a discussion of open research problems and future research directions in the light of the presented survey.

Networked Radar Mission Planning Algorithm Based on Pulse Interleaving and Cross-Scheduling Interval

“…After analyzing all the tasks in the application task list Q in the current scheduling interval, if there are still remaining idle time fragments in the interval, search for search tasks that can be advanced to this scheduling interval from subsequent scheduling intervals on the entire time axis, regardless of the time offset of their tasks. These search tasks can be directly inserted into the idle time fragments, thus improving the utilization rate of time resources [12].…”