Radar active antagonism through deep reinforcement learning: A Way to address the challenge of mainlobe jamming

Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming

“…Pulse-level FA radar has the capability of changing carrier frequency randomly from pulse to pulse, which imparts the radar with a good ECCM capability [27]. However, if the jammer can react to the current intercepted radar pulse, then the ECCM performance of the pulse-level FA radar will degrade [16]. To improve the ECCM performance against the jammer mentioned above, a subpulse-level frequency-agile waveform [9] was adopted in this paper.…”

“…In contrast to the signal-to-noise ratio (SNR) reward signal used in [10], the authors in [11] proposed utilizing the probability of detection as the reward signal, and a similar deep RL-based antijamming scheme for FA radar was proposed. In contrast to the pulse-level FA radar in [10,11], subpulse-level FA radar and a jammer that works in a transmit/receive time-sharing mode were considered in [16], which is more similar to real electronic warfare than the scenarios in [10,11]. In addition, a policy gradient-based RL algorithm known as proximal policy optimization (PPO) [12] was used in [16] to further facilitate the stability of the learning process and improve convergence performance.…”

“…In contrast to the pulse-level FA radar in [10,11], subpulse-level FA radar and a jammer that works in a transmit/receive time-sharing mode were considered in [16], which is more similar to real electronic warfare than the scenarios in [10,11]. In addition, a policy gradient-based RL algorithm known as proximal policy optimization (PPO) [12] was used in [16] to further facilitate the stability of the learning process and improve convergence performance. In [13], the antijamming strategy design was investigated under a partially observable condition, and the authors highlighted that antijamming performance depends on the random nature of the jammer.…”

“…As discussed in [10,11,16], FA radar can learn antijamming strategies offline in the training environment and then utilize the learned strategies to combat the jammer in the test environment. At every time step, the jammer will intercept the action of the radar, and the radar will also sense the whole electronic spectrum to infer the action of the jammer.…”

“…At every time step, the jammer will intercept the action of the radar, and the radar will also sense the whole electronic spectrum to infer the action of the jammer. The sensing in these procedures was assumed to be accurate and perfect in the training environment in [10,11,16]. This assumption is not always true in practice because uncertainties exist in both the radar and jammer.…”