Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection
Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone

TL;DR
This paper introduces a linear contextual bandit approach for adaptive radar waveform selection, leveraging spectrum sensing and feedback to improve target detection in various environments, including adversarial scenarios.
Contribution
It formulates the waveform selection as a linear contextual bandit problem, enabling computationally feasible and sample-efficient adaptive radar strategies.
Findings
Significant performance improvements in target detection with bandit algorithms.
Effective handling of adversarial environments with stochastic and adversarial models.
Mitigation of pulse-agile impacts through time-varying waveform constraints.
Abstract
A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an…
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