Adversarial Multi-Player Bandits for Cognitive Radar Networks

TL;DR

This paper models a radar network as an adversarial bandit problem, demonstrating that multi-player bandit algorithms enable the network to maintain target tracking accuracy in challenging adversarial environments.

Contribution

It introduces the application of adversarial multi-player bandit algorithms to radar networks, showing their effectiveness in maintaining tracking performance under adversarial conditions.

Findings

Simple sub-band selection algorithms fail in adversarial environments.

Multi-player bandit algorithms enable continuous target tracking.

Radar networks can sustain high SINR and tracking precision adversarial settings.

Abstract

We model a radar network as an adversarial bandit problem, where the environment pre-selects reward sequences for each of several actions available to the network. This excludes environments which vary rewards in response to the learner's actions. Adversarial environments include those with third party emitters which enter and exit the environment according to some criteria which does not depend on the radar network. The network consists of several independent radar nodes, which attempt to attain the highest possible SINR in each of many time steps. We show that in such an environment, simple sub-band selection algorithms are unable to consistently attain high SINR. However, through the use of adversarial multi-player bandit algorithms, a radar network can continue to track targets without a loss in tracking precision.