  Object detection utilizing Frequency Modulated Continous Wave radar is
becoming increasingly popular in the field of autonomous systems. Radar does
not possess the same drawbacks seen by other emission-based sensors such as
LiDAR, primarily the degradation or loss of return signals due to weather
conditions such as rain or snow. However, radar does possess traits that make
it unsuitable for standard emission-based deep learning representations such as
point clouds. Radar point clouds tend to be sparse and therefore information
extraction is not efficient. To overcome this, more traditional digital signal
processing pipelines were adapted to form inputs residing directly in the
frequency domain via Fast Fourier Transforms. Commonly, three transformations
were used to form Range-Azimuth-Doppler cubes in which deep learning algorithms
could perform object detection. This too has drawbacks, namely the
pre-processing costs associated with performing multiple Fourier Transforms and
normalization. We explore the possibility of operating on raw radar inputs from
analog to digital converters via the utilization of complex transformation
layers. Moreover, we introduce hierarchical Swin Vision transformers to the
field of radar object detection and show their capability to operate on inputs
varying in pre-processing, along with different radar configurations, i.e.
relatively low and high numbers of transmitters and receivers, while obtaining
on par or better results than the state-of-the-art.
