Reinforcement Learning based Anti-jamming Frequency Hopping Strategies Design for Cognitive Radar

Artificial Intelligence Aided Electronic Warfare Systems- Recent Trends and Evolving Applications

“…In this direction, a technique of chaff jamming recognition based on the SVM classification method is presented in [60]. In [61] anti-jamming designs because of its ability to randomly alter the carrier frequency [62]. In this direction, a reinforcement learning-based anti-jamming frequency hopping algorithm for cognitive radar is presented in [62].…”

“…In [61] anti-jamming designs because of its ability to randomly alter the carrier frequency [62]. In this direction, a reinforcement learning-based anti-jamming frequency hopping algorithm for cognitive radar is presented in [62]. Q-learning and deep Q-network (DQN) is used in this method.…”

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

“…That is to say, 100m of y-axis and 1m of x-axis have the same length in Figure 7. [2,18], [4,18], [6,18], [8,18], [10,18], [12,18] [x (1 m),y (100 m)]…”

“…Transmitting Frequency of Jammer 1 9-9.8 GHz Position of Jammer 1 [2,4], [4,4], [6,4], [8,4], [10,4] When the detection requirements are different, the networking topology of radar UAV swarms is different. Considering the problem of radar target location, this section presents two scenarios to verify the effectiveness of the algorithm.…”

“…Machine learning: intelligent countermeasures represented by reinforcement learning have been widely used in recent years. Reinforcement learning realizes the policy iteration through the interaction between agent and environment and finally generates a reasonable policy set [8,9]. With the progress of electronic jamming, the traditional jammer with single function and fixed working mode is promoted to intelligent jamming of multi-function, multi-parameter and variable working mode [10].…”