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Predicting Cognitive Emitter Behavior with GAIL.
| 题名： | Predicting Cognitive Emitter Behavior with GAIL. |
| 作者： | Tai, A. |
| 摘要： | Through interactions with the surroundings, modern cognitive radar systems learn to optimize their decision-making process to intelligently select transmission waveforms and operating parameters. Because of their waveform agility and the ability to respond dynamically, cognitive radars are difficult to track and disrupt. This work aims to explore if Generative Adversarial Imitation Learning (GAIL) can be applied to capture, imitate, and predict the behavior of cognitive radars. We study the basic principles of GAIL, explore its existing applications, and research implementation of the approach for tracking the actions of self-driving cars. We conclude with the feasibility analysis of utilizing GAIL for predicting the behavior of cognitive radar systems. |
| 总页数： | 5 pages |
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