基于强化学习的雷达自适应波形设计，提升弱信号目标追踪精度
Power-Aware Cognitive Radar Multi-target Tracking Under Unknown Disturbances
- 用POMCP动态优化波形分配，逐目标构建状态预测树
- 低信噪比目标检测率从0.6提升至近0.9，最弱目标跟踪更准
- 适合复杂干扰下多目标追踪场景，尤其关注弱信号目标
本文提出一种认知雷达（CR）框架，利用大规模多输入多输出（MMIMO）系统在未知干扰下追踪多个飞行器。由于在不同信噪比（SNR）条件下均一功率分配次优，我们采用由部分可观测蒙特卡洛规划（POMCP）驱动的自适应波形设计。为每个目标独立构建POMCP树，系统可高效预测目标状态。这些预测结果用于求解约束优化问题，主动将发射能量集中于较弱目标，同时保障强目标的足够功率。实验表明，所提POMCP方法使低信噪比目标的检测概率从0.6提升至接近0.9，并在最弱目标的跟踪精度上优于非自适应正交波形或认知均匀功率POMCP基线。
原文摘要 · Abstract (English)
This work presents a cognitive radar (CR) framework designed to track multiple aircraft under unknown disturbances using massive multiple-input multiple-output (MMIMO) systems. Since uniform power allocation is suboptimal across varying signal-to-noise ratios (SNRs), we couple an adaptive waveform design driven by Partially Observable Monte Carlo Planning (POMCP). By assigning an independent POMCP tree to each target, the system efficiently predicts target states. These predictions inform a constrained optimization problem that actively directs transmit energy toward weaker targets while maintaining sufficient power for stronger ones. Results confirm that the proposed POMCP method improves the detection probability for low-SNR targets from 0.6 to nearly 0.9, and yields more accurate tracking of the weakest target than a non-adaptive orthogonal waveform or a cognitive uniform-power POMCP baseline.
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