  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.
