Deep Learning-Based Activity Monitoring for Smart Environment Using Radar

Abstract

Monitoring the environment plays a significant role in the evolution of a smart city with enhanced safety, comfort, and security. One promising technology for smart environment monitoring is the radar-based moving object tracking and classification. Radar-based technology is known to provide better solutions compared to vision-based systems as the latter is prone to the adverse effects of lighting and weather conditions. Radars operating in mm-wave range are preferred due to small size, good angular resolution, small apertures, and low cost. The signature waveform received from the moving object is used to estimate the range and the velocity of multiple targets based on Doppler drift. Also, the micro-Doppler effect on the return signal that occurs due to the micromotion dynamics such as vibrations or rotations can be used to identify the specific type of the target. This book chapter provides an overview of different types of radars and their respective signal processing aspects used for detecting the human and animal activity. Moreover, this chapter explains the deep learning algorithms used for detection and classification of human and animal activity. Thus the intelligent radar systems add greater value to the smart environment compared to other non-radar methods.