  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.
