  This paper describes important considerations and challenges associated with
online reinforcement-learning based waveform selection for target
identification in frequency modulated continuous wave (FMCW) automotive radar
systems. We present a novel learning approach based on satisficing Thompson
sampling, which quickly identifies a waveform expected to yield satisfactory
classification performance. We demonstrate through measurement-level
simulations that effective waveform selection strategies can be quickly
learned, even in cases where the radar must select from a large catalog of
candidate waveforms. The radar learns to adaptively select a bandwidth for
appropriate resolution and a slow-time unimodular code for interference
mitigation in the scene of interest by optimizing an expected classification
metric.
