  In tracking radar, the sensing environment often varies significantly over a
track duration due to the target's trajectory and dynamic interference.
Adapting the radar's waveform using partial information about the state of the
scene has been shown to provide performance benefits in many practical
scenarios. Moreover, radar measurements generally exhibit strong temporal
correlation, allowing memory-based learning algorithms to effectively learn
waveform selection strategies. This work examines a radar system which builds a
compressed model of the radar-environment interface in the form of a
context-tree. The radar uses this context tree-based model to select waveforms
in a signal-dependent target channel, which may respond adversarially to the
radar's strategy. This approach is guaranteed to asymptotically converge to the
average-cost optimal policy for any stationary target channel that can be
represented as a Markov process of order U < $\infty$, where the constant U is
unknown to the radar. The proposed approach is tested in a simulation study,
and is shown to provide tracking performance improvements over two
state-of-the-art waveform selection schemes.
