A key component of cognitive radar is the ability to generalize, or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies which are effective in one scene may be highly suboptimal in another. We address this problem by strategically biasing a learning algorithm by exploiting high-level structure across tracking instances, referred to as meta-learning. In this work, we develop an online meta-learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning, and provide prior-dependent performance bounds for the meta-learning problem using Probability Approximately Correct (PAC)-Bayes theory. We present a computationally feasible meta-posterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online meta-learning for waveform-agile tracking.

翻译：认知雷达的关键能力在于泛化能力，即在多种感知环境中保持稳定性能，因为物理场景的各方面因素可能随时间变化。这对基于学习的波形选择方法构成了挑战：在某一场景中有效的传输策略在另一场景中可能高度次优。我们通过策略性地利用跨跟踪实例的高层结构（称为元学习）来偏置学习算法，从而解决这一问题。本文提出了一种在线元学习方法，用于波形敏捷跟踪。该方法利用先前目标跟踪中获取的信息，加速并增强新跟踪实例的学习过程。通过挖掘跟踪场景间固有的相似性（归因于目标类型或杂波统计等共同物理要素），我们实现了在有限状态目标信道类别上的高效样本学习。我们将在线波形选择问题构建于贝叶斯学习框架内，并利用概率近似正确（PAC）-贝叶斯理论推导出元学习问题的先验相关性能界。我们提出了一种计算可行的元后验采样算法，并通过包含多种场景的仿真研究评估其性能。最后，我们探讨了在线元学习在波形敏捷跟踪中的潜在性能优势及实际挑战。