标题： 认知雷达何时有益？ 显示英文标题

标题： When is Cognitive Radar Beneficial?

摘要： 在什么情况下基于在线强化学习的频率敏捷认知雷达应该预期优于基于规则的自适应波形选择策略？ 我们通过检查一个动态频谱接入场景来寻求对此问题的见解，其中雷达希望在每个脉冲重复间隔内传输最宽的未占用带宽。 将在线学习与固定规则的感知和规避策略进行比较。 我们表明，在给定一个简单的马尔可夫信道模型的情况下，可以通过随机优势对简单情况进行分析研究。 此外，我们表明，在更现实的信道假设下，基于学习的方法表现出更强的泛化能力。 然而，对于时间范围较短且定义良好的问题，我们发现机器学习方法可能由于收敛时间的固有局限性而表现不佳。 我们得出结论，说明何时基于学习的方法预期有益，并为未来的研究提供指导。 显示英文摘要

摘要： 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.