  Pulse-agile radar systems have demonstrated favorable performance in dynamic
electromagnetic scenarios. However, the use of non-identical waveforms within a
radar's coherent processing interval may lead to harmful distortion effects
when pulse-Doppler processing is used. This paper presents an online learning
framework to optimize detection performance while mitigating harmful sidelobe
levels. The radar waveform selection process is formulated as a linear
contextual bandit problem, within which waveform adaptations which exceed a
tolerable level of expected distortion are eliminated. The constrained online
learning approach is effective and computationally feasible, evidenced by
simulations in a radar-communication coexistence scenario and in the presence
of intentional adaptive jamming. This approach is applied to both stochastic
and adversarial contextual bandit learning models and the detection performance
in dynamic scenarios is evaluated.
