Toward Deep Learning-Based Human Target Analysis

Abstract

In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration.

Unobtrusive human activity classification based on combined time‐range and time‐frequency domain signatures using ultrawideband radar

“…preprocessing stage. For further information about UWB radar signal modelling for human motion detection and classification, see Mostafa et al [45], Sachs and Herrmann [55] and He et al [56].…”

“…T A B L E 2 F I G U R E 3 Human activity: (a) Fall forward; (b) Fall backward; (c) Fall left; (d) Fall right; (e) Sit; (f) Stand; (g) Walk; and (h) Bend MOSTAFA AND CHAMAANI -5signal modelling for human motion detection and classification, see Mostafa et al[45], Sachs and Herrmann[55] and He et al[56].…”