tinyML Asia 2021
Learning for Enhanced Vision
Ankit SHARMA, System Architect,
Modern-day radar is quickly leaping ahead of being a 4-D imaging device, let alone 3-D imaging. The fine resolution offered by millimeter-wave radars has pushed the limits of imaging to 5-D, where the fifth dimension refers to the type/class of the object imaged by the radar. This brings in strong use cases of supervised/unsupervised learning to be used at an imaging radar. Though machine learning in its various forms could be used in estimating the traditional parameter set of radars such as the range, Doppler velocity, and direction-of-arrival (DoA), estimating object dimensions; shapes, orientations, and types open a very relevant problem set to be solved by supervised learning. For traffic enforcement applications this could mean deciphering the vehicle type and for autonomous driving, this could mean classifying the object type as road/pedestrian/car/bus and taking appropriate action. These applications through traditional for the field of image signal processing, are quite novel to radar signal processing. As such, applications of machine learning in augmenting new dimensions to an imaging radar are crucial solution differentiators. To this end, we propose an application of machine learning to classify objects detected by the radar from an array of predefined classes. The classification algorithm runs in real-time and uses primarily the point cloud detected by the radar and reflectivity of Electro-Magnetic (EM) waves at 80GHz as inputs. The classification rate thus obtained is quantified and shown as an accuracy measure.