  Convolutional neural networks have often been proposed for processing radar
Micro-Doppler signatures, most commonly with the goal of classifying the
signals. The majority of works tend to disregard phase information from the
complex time-frequency representation. Here, the utility of the phase
information, as well as the optimal format of the Doppler-time input for a
convolutional neural network, is analysed. It is found that the performance
achieved by convolutional neural network classifiers is heavily influenced by
the type of input representation, even across formats with equivalent
information. Furthermore, it is demonstrated that the phase component of the
Doppler-time representation contains rich information useful for classification
and that unwrapping the phase in the temporal dimension can improve the results
compared to a magnitude-only solution, improving accuracy from 0.920 to 0.938
on the tested human activity dataset. Further improvement of 0.947 is achieved
by training a linear classifier on embeddings from multiple-formats.
