  Millimeter-wave (mmW) radars are being increasingly integrated in commercial
vehicles to support new Adaptive Driver Assisted Systems (ADAS) for its ability
to provide high accuracy location, velocity, and angle estimates of objects,
largely independent of environmental conditions. Such radar sensors not only
perform basic functions such as detection and ranging/angular localization, but
also provide critical inputs for environmental perception via object
recognition and classification. To explore radar-based ADAS applications, we
have assembled a lab-scale frequency modulated continuous wave (FMCW) radar
test-bed (https://depts.washington.edu/funlab/research) based on Texas
Instrument's (TI) automotive chipset family. In this work, we describe the
test-bed components and provide a summary of FMCW radar operational principles.
To date, we have created a large raw radar dataset for various objects under
controlled scenarios. Thereafter, we apply some radar imaging algorithms to the
collected dataset, and present some preliminary results that validate its
capabilities in terms of object recognition. Our code is available at
https://github.com/Xiangyu-Gao/mmWave-radar-signal-processing-and-microDoppler-classification.
