Surface Classification with Millimeter-Wave Radar for Constant Velocity Devices using Temporal Features and Machine Learning

Abstract: Classification of surfaces in the near field using millimeter-wave radar commonly considers the use of polarization based methods for road condition monitoring. When a surface consists of larger structures one instead wishes to monitor the surface topography. Analysis of scattering from rough surfaces is highly complex and relies on prior knowledge of surface structure. In this work a device moving at constant velocity is considered. By constructing a set of slow and fast time based features a machine learning classifier is used to distinguish grass target surfaces from asphalt, gravel, soil and tiled surfaces. It is found that using estimated autocovariances and average envelope shapes make for efficient features and that a small fully connected neural network classifier adequately manages to determine the surface type. The found model is accurate yet parsimonious and could be implemented with limited hardware requirements. Application of a median filter onto the sequence of classifier predictions effectively suppresses outlying predictions. This model can find use in autonomous devices that have tasks performed on designated surface types, such as in autonomous lawn mowers.