论文标题
受到限制的在线学习以减轻脉搏敏捷认知雷达中的失真效果
Constrained Online Learning to Mitigate Distortion Effects in Pulse-Agile Cognitive Radar
论文作者
论文摘要
在动态电磁场景中，脉搏敏锐的雷达系统表现出良好的性能。但是，当使用脉冲多普勒处理时，在雷达相干处理间隔内使用非相同波形可能会导致有害的失真效应。本文提出了一个在线学习框架，以优化检测性能，同时减轻有害的侧虫水平。雷达波形选择过程被表述为线性上下文匪徒问题，其中消除了超过预期失真水平的波形适应。受约束的在线学习方法是有效的，并且在计算上是可行的，这在雷达通信共存方案中的模拟和有意自适应干扰的情况下证明了这一点。这种方法应用于随机和对抗性上下文的匪徒学习模型，并评估了动态场景中的检测性能。
Pulse-agile radar systems have demonstrated favorable performance in dynamic electromagnetic scenarios. However, the use of non-identical waveforms within a radar's coherent processing interval may lead to harmful distortion effects when pulse-Doppler processing is used. This paper presents an online learning framework to optimize detection performance while mitigating harmful sidelobe levels. The radar waveform selection process is formulated as a linear contextual bandit problem, within which waveform adaptations which exceed a tolerable level of expected distortion are eliminated. The constrained online learning approach is effective and computationally feasible, evidenced by simulations in a radar-communication coexistence scenario and in the presence of intentional adaptive jamming. This approach is applied to both stochastic and adversarial contextual bandit learning models and the detection performance in dynamic scenarios is evaluated.
