Generating NLFM Radar Waveforms using Variational Autoencoders

The rapid synthesis of radar waveform modulations is key to enabling a radar to react to the environment in order to optimize performance. This paper proposes the use of generative models for radar waveform generation. Specifically, variational autoencoders (VAEs) comprising neural networks that are trained with a novel reconstruction loss are proposed. It is shown for simple classes of non-linear FM waveforms that the decoder from the proposed VAE can generate new radar waveform modulations that possess required ambiguity function characteristics, even though they were not represented in the training data.