论文标题
认知雷达何时有益？
When is Cognitive Radar Beneficial?
论文作者
论文摘要
何时应何时将基于在线学习的频率敏捷认知雷达胜过基于规则的自适应波形选择策略？我们通过检查动态频谱访问方案寻求有关此问题的见解，在该场景中，雷达希望在每个脉冲重复间隔内以最宽的无空间带宽传输。将在线学习与固定的基于规则的感官和避免策略进行了比较。我们表明，给定一个简单的马尔可夫通道模型，可以通过随机优势对简单案例进行分析检查问题。此外，我们表明，对于更现实的渠道假设，基于学习的方法表明了更大的概括能力。但是，对于明确指定的短时间问题，我们发现由于收敛时间的固有局限性，机器学习方法的表现可能会很差。我们得出结论，即何时预期基于学习的方法是有益的，并为未来的研究提供指南。
When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.
