Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar

TL;DR

This paper models the radar-jammer interaction as a multi-round extensive-form game with imperfect information and applies deep Counterfactual Regret Minimization to efficiently approximate Nash Equilibrium strategies.

Contribution

It introduces a novel game-theoretic framework for radar-jammer competition and employs deep CFR to solve the complex game efficiently.

Findings

Deep CFR effectively approximates NE strategies.

The game model captures multi-round radar-jammer interactions.

Numerical results validate the approach's effectiveness.

Abstract

The competition between radar and jammer is one emerging issue in modern electronic warfare, which in principle can be viewed as a non-cooperative game with two players. In this work, the competition between a frequency agile (FA) radar and a noise-modulated jammer is considered. As modern FA radar adopts coherent processing with several pulses, the competition is hence in a multiple-round way where each pulse can be modeled as one round interaction between the radar and jammer. To capture such multiple-round property as well as imperfect information inside the game, i.e., radar and jammer are unable to know the upcoming signal, we propose an extensive-form game formulation for such competition. Since the number of game information states grows exponentially with respect to number of pulses, finding Nash Equilibrium (NE) strategies may be a computationally intractable task. To effectively solve the game, a learning-based algorithm called deep Counterfactual Regret Minimization (CFR) is utilized. Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.