Online waveform selection for cognitive radar

TL;DR

This work proposes adaptive algorithms that select waveform parameters in an online fashion using domain knowledge derived from the characteristics of ballistic trajectories and proposes three reinforcement learning algorithms: bandwidth scaling, Q-learning, and Q-learning lookahead.

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.

Figures (4)

• Figure 1: Average MSE in range over 100 runs vs. No. of transmitted beams illuminated on target
• Figure 2: Range window and innovation vs. No. of transmissions
• Figure 3: Histogram of the number of beams before the target was lost over 100 runs
• Figure 4: Average MSE in range over 100 runs vs. No. of transmitted beams illuminated on target for an unseen trajectory