  The problem of data-driven joint design of transmitted waveform and detector
in a radar system is addressed in this paper. We propose two novel
learning-based approaches to waveform and detector design based on end-to-end
training of the radar system. The first approach consists of alternating
supervised training of the detector for a fixed waveform and reinforcement
learning of the transmitter for a fixed detector. In the second approach, the
transmitter and detector are trained simultaneously. Various operational
waveform constraints, such as peak-to-average-power ratio (PAR) and spectral
compatibility, are incorporated into the design. Unlike traditional radar
design methods that rely on rigid mathematical models with limited
applicability, it is shown that radar learning can be robustified by training
the detector with synthetic data generated from multiple statistical models of
the environment. Theoretical considerations and results show that the proposed
methods are capable of adapting the transmitted waveform to environmental
conditions while satisfying design constraints.
