  mmWave FMCW radar has attracted huge amount of research interest for
human-centered applications in recent years, such as human gesture/activity
recognition. Most existing pipelines are built upon conventional Discrete
Fourier Transform (DFT) pre-processing and deep neural network classifier
hybrid methods, with a majority of previous works focusing on designing the
downstream classifier to improve overall accuracy. In this work, we take a step
back and look at the pre-processing module. To avoid the drawbacks of
conventional DFT pre-processing, we propose a learnable pre-processing module,
named CubeLearn, to directly extract features from raw radar signal and build
an end-to-end deep neural network for mmWave FMCW radar motion recognition
applications. Extensive experiments show that our CubeLearn module consistently
improves the classification accuracies of different pipelines, especially
benefiting those previously weaker models. We provide ablation studies on
initialization methods and structure of the proposed module, as well as an
evaluation of the running time on PC and edge devices. This work also serves as
a comparison of different approaches towards data cube slicing. Through our
task agnostic design, we propose a first step towards a generic end-to-end
solution for radar recognition problems.
