在线选择雷达波形参数，提升导弹全程跟踪精度与连续性
Online waveform selection for cognitive radar
- 基于弹道轨迹特性设计强化学习算法，实时调整波形带宽
- 在合成弹道数据上实现范围误差最小化与目标不丢失
- 适用于需要持续高精度跟踪的复杂动态场景
设计能够自适应参数的认知雷达系统极具挑战性，尤其在全程追踪弹道导弹时。本文提出三种在线自适应算法：带宽缩放、Q-learning 和 Q-learning 预览，利用弹道轨迹的领域知识建模学习问题。这些算法根据接收反馈动态选择每次发射的带宽。在合成生成的弹道轨迹实验中，所提方法成功实现范围误差最小化与持续跟踪目标，避免丢失目标。
原文摘要 · Abstract (English)
Designing a cognitive radar system capable of adapting its parameters is challenging, particularly when tasked with tracking a ballistic missile throughout its entire flight. In this work, we focus on proposing adaptive algorithms that select waveform parameters in an online fashion. Our novelty lies in formulating the learning problem using domain knowledge derived from the characteristics of ballistic trajectories. We propose three reinforcement learning algorithms: bandwidth scaling, Q-learning, and Q-learning lookahead. These algorithms dynamically choose the bandwidth for each transmission based on received feedback. Through experiments on synthetically generated ballistic trajectories, we demonstrate that our proposed algorithms achieve the dual objectives of minimizing range error and maintaining continuous tracking without losing the target.
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