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:认知雷达的一个关键组成部分是能够对各种感测环境进行普及或取得一致的性能,因为物理场景的各个方面可能随时间而变化。这给学习的波形选择方法带来了挑战,因为在一个场景中有效的传输政策可能极不理想。我们通过利用跨跟踪的高级结构(称为元学习)来从战略上偏向学习算法来解决这一问题。在这项工作中,我们开发了一种波形跟踪的在线元学习方法。这一方法利用从先前的目标轨道获得的信息来加快和加强在新跟踪案例中的学习。这导致通过利用不同跟踪场景的内在相似性,在一定的州目标渠道中进行抽样高效学习,这归因于目标类型或结晶统计等共同的物理要素。我们在巴伊西亚学习框架内制定在线波形选择问题,并利用概率大约正确(PAC)-Bayyes理论为元学习问题提供以前依赖的性能约束。我们用计算可行的元子取样算出一种可行的元子抽样抽样分析算法,并用模拟学习方法研究包括不同波形模型学习潜力的在线学习的绩效。最后,我们用模拟模型研究研究与各种模型学习挑战。