Online waveform selection for cognitive radar

TL;DR

This paper introduces adaptive online waveform selection algorithms for cognitive radar, utilizing reinforcement learning to improve ballistic missile tracking by dynamically adjusting transmission bandwidth based on feedback.

Contribution

It formulates a novel reinforcement learning framework incorporating domain knowledge for waveform selection in ballistic missile tracking.

Findings

Algorithms effectively minimize range error.

Maintain continuous target tracking.

Demonstrated success on synthetic ballistic trajectories.

Abstract

Designing a cognitive radar system capable of adapting its parameters is challenging, particularly when tasked with tracking a ballistic missile throughout its entire flight. In this work, we focus on proposing adaptive algorithms that select waveform parameters in an online fashion. Our novelty lies in formulating the learning problem using domain knowledge derived from the characteristics of ballistic trajectories. We propose three reinforcement learning algorithms: bandwidth scaling, Q-learning, and Q-learning lookahead. These algorithms dynamically choose the bandwidth for each transmission based on received feedback. Through experiments on synthetically generated ballistic trajectories, we demonstrate that our proposed algorithms achieve the dual objectives of minimizing range error and maintaining continuous tracking without losing the target.