  An end-to-end learning approach is proposed for the joint design of
transmitted waveform and detector in a radar system. Detector and transmitted
waveform are trained alternately: For a fixed transmitted waveform, the
detector is trained using supervised learning so as to approximate the
Neyman-Pearson detector; and for a fixed detector, the transmitted waveform is
trained using reinforcement learning based on feedback from the receiver. No
prior knowledge is assumed about the target and clutter models. Both
transmitter and receiver are implemented as feedforward neural networks.
Numerical results show that the proposed end-to-end learning approach is able
to obtain a more robust radar performance in clutter and colored noise of
arbitrary probability density functions as compared to conventional methods,
and to successfully adapt the transmitted waveform to environmental conditions.
