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In tracking radar, the sensing environment often varies significantly over atrack duration due to the target's trajectory and dynamic interference.Adapting the radar's waveform using partial information about the state of thescene has been shown to provide performance benefits in many practicalscenarios. Moreover, radar measurements generally exhibit strong temporalcorrelation, allowing memory-based learning algorithms to effectively learnwaveform selection strategies. This work examines a radar system which builds acompressed model of the radar-environment interface in the form of acontext-tree. The radar uses this context tree-based model to select waveformsin a signal-dependent target channel, which may respond adversarially to theradar's strategy. This approach is guaranteed to asymptotically converge to theaverage-cost optimal policy for any stationary target channel that can berepresented as a Markov process of order U < $\infty$, where the constant U isunknown to the radar. The proposed approach is tested in a simulation study,and is shown to provide tracking performance improvements over twostate-of-the-art waveform selection schemes.

Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning