Deep Reinforcement Learning-Based Radar LPI Strategy for Antijamming and Target Detection

The low probability of intercept (LPI) strategy can significantly reduce the probability of suffering electronic intelligence (ELINT) and jamming for radar. However, some unreasonable LPI designs may seriously weaken the target detection and tracking performance of the radar. Thus, this paper explores a deep reinforcement learning (DRL)-based optimization strategy for radar LPI signals to better counter jamming systems. The entire optimization process is modeled as a Markov decision process (MDP) and implemented via a double-deep Q-learning network (DDQN) through online interaction with the unknown jamming environment. Firstly, the strategy provides three actions of transmit power, modulation type, and carrier frequency for radar agent to evade the reconnaissance by the ELINT system, which create more challenges for the entire workflow of the ELINT system. Thereafter, a reward function with freely adaptable weights is designed to guide the DRL algorithm for dynamically balancing the LPI performance and the coherent integration (CI) performance of the radar, where the signal strategy duration and the transmit power are focused on. The simulations demonstrate that the proposed method performs well in the unknown environment, and the detection performance of the radar is proved by performing cell averaging constant false alarm rate (CA-CFAR) detection on the range-Doppler (R-D) images. When less than 1% of the pulses are jammed, the proposed strategy can get higher CI performance than the compared strategy. This work can provide a dynamic strategy for radar pulses and ensure radar target detection performance in electronic warfare.