Radar technology has evolved considerably in the last few decades. There are many areas
where radar systems are applied, including air traffic control in airports, ocean surveillance, and
research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking
sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving
rise to myriad new applications, from the recognition of the material close objects are made, to the
recognition of hand gestures. They have also been recently used to identify how a person interacts
with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case,
the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands
or the digital device. This paper presents a thoughtful comparative analysis of different feature
extraction techniques and classification strategies applied on a series of datasets that cover problems
such as the identification of materials, element counting, or determining the orientation and distance
of objects to the sensor. The results outperform previous works using these datasets, especially when
the accuracy was lowest, showing the benefits feature extraction techniques have on classification
performance.
