  A fundamental problem for waveform-agile radar systems is that the true
environment is unknown, and transmission policies which perform well for a
particular tracking instance may be sub-optimal for another. Additionally,
there is a limited time window for each target track, and the radar must learn
an effective strategy from a sequence of measurements in a timely manner. This
paper studies a Bayesian meta-learning model for radar waveform selection which
seeks to learn an inductive bias to quickly optimize tracking performance
across a class of radar scenes. We cast the waveform selection problem in the
framework of sequential Bayesian inference, and introduce a contextual bandit
variant of the recently proposed meta-Thompson Sampling algorithm, which learns
an inductive bias in the form of a prior distribution. Each track is treated as
an instance of a contextual bandit learning problem, coming from a task
distribution. We show that the meta-learning process results in an appreciably
faster learning, resulting in significantly fewer lost tracks than a
conventional learning approach equipped with an uninformative prior.
