Design of Jamming-Detection Shared Signal Based on Deep Reinforcement Learning

Owing to the increasing intelligence of radar electronic warfare systems，traditional jammers cannot adapt to the changes in the environment，which greatly reduces their effectiveness. The detection signal can be hidden in the jamming signal to construct a jamming-detection shared signal so that the jamming signal sent by the reconnaissance jammer equipment has a detection effect. In this paper，a jamming-detection shared signal based on a multi-carrier phase code(MCPC)is designed to solve the problems of low complexity and narrow spectrum width of the current jamming-detection shared signal. This signal features good noise-like wide spectrum characteristics，good distance detection capacities，and good speed detection capacities. Moreover，it can suppress the jamming on the target radar and covertly detect the target signal and surrounding environment. To adapt the shared signal to the perception and activity of the battlefield environment，a deep reinforcement learning algorithm is introduced to optimize the shared signal of MCPC. The Q-value is first regularized using the dueling deep Q-learning network，which solves the local optimization problem caused by the overestimation in the network. A state value function is then introduced into the reward value to form a composite reward，which is referred to as the composite reward-dueling deep Q-learning network based on regulation(CR-DuDQNReg). The sensitivity of the MCPC shared signals to the reward value can then be adjusted according to the signal’s own state，and the initial phase code value can be adaptively optimized to suppress interference and improve covert detection. The experimental results showed that the maximum spectrum amplitude of the MCPC signal optimized using CR-DuDQNReg was increased by 17.48%，the maximum pulse compression amplitude was increased by 17.25%，the first side lobe amplitude of the Doppler ambiguity function was reduced by 12.69%，and the optimization effect was better than that of the traditional deep reinforcement learning algorithm. © 2023 Tianjin University. All rights reserved.