Quality of service based radar resource management using deep reinforcement learning

TL;DR

This paper introduces a deep reinforcement learning approach to enhance real-time radar resource management, significantly improving the efficiency of quality of service-based decision making in cognitive radar systems.

Contribution

The paper presents a novel deep reinforcement learning solution for Q-RAM, enabling faster and more effective real-time radar resource allocation.

Findings

Improved runtime performance over classical methods

Enhanced decision-making efficiency in radar systems

Potential for real-time application in cognitive radars

Abstract

An intelligent radar resource management is an essential milestone in the development of a cognitive radar system. The quality of service based resource allocation model (Q-RAM) is a framework allowing for intelligent decision making but classical solutions seem insufficient for real-time application in a modern radar system. In this paper, we present a solution for the Q-RAM radar resource management problem using deep reinforcement learning considerably improving on runtime performance.