Jamming Prediction for Radar Signals Using Machine Learning Methods Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long shortterm memory (LSTM) which takes the PDW list as input. Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs. Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library. The prediction performance and time complexity of two proposed methods are compared. In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.

A FrequencyHopping Game Between a Smart Jammer and a Cognitive Radio A smart jammer can determine the frequencyhopping patterns of a cognitive radio using machine learning. This paper develops a twostate stochastic game where the receiver and jammer choose at each stage whether to stay on their current frequency or hop to a new one. The cost of hopping to a new frequency is also modeled with a discounted payoff. Machine learning algorithms are implemented to update the policies for both players in order to converge to a stationary Nash equilibrium. The game is simulated using Nash Qlearning and WoLF PHC, as well as the computed singlestage Nash equilibrium for each state. Of the strategy options included for both players, there are ideal strategyupdating algorithm pairs depending on the values used for the discounted payoffs.