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An end-to-end learning approach is proposed for the joint design oftransmitted waveform and detector in a radar system. Detector and transmittedwaveform are trained alternately: For a fixed transmitted waveform, thedetector is trained using supervised learning so as to approximate theNeyman-Pearson detector; and for a fixed detector, the transmitted waveform istrained using reinforcement learning based on feedback from the receiver. Noprior knowledge is assumed about the target and clutter models. Bothtransmitter and receiver are implemented as feedforward neural networks.Numerical results show that the proposed end-to-end learning approach is ableto obtain a more robust radar performance in clutter and colored noise ofarbitrary probability density functions as compared to conventional methods,and to successfully adapt the transmitted waveform to environmental conditions.

End-to-end Learning of Waveform Generation and Detection for Radar Systems