A 3D Non-Stationary Cluster Channel Model for Human Activity Recognition

Abstract

This paper proposes a three-dimensional (3D) non- stationary fixed-to-fixed indoor channel simulator model for human activity recognition. The channel model enables the formulation of temporal variations of the received signal caused by a moving human. The moving human is modelled by a cluster of synchronized moving scatterers. Each of the moving scatterers in a cluster is described by a 3D deterministic trajectory model representing the motion of specific body parts of a person, such as wrists, ankles, head, and waist. We derive the time-variant (TV) Doppler frequencies caused by the motion of each moving scatterer by using the TV angles of motion, angles of arrival, angles of departure. Moreover, we derive the complex channel gain of the received signal. Furthermore, we analyze the TV Doppler power spectral density of the complex channel gain by using the concept of the spectrogram and present its expression in approximated form. Also, we derive the TV mean Doppler shift and TV Doppler spread from the approximated spectrogram. The accuracy of the results is validated by simulations. The channel simulator is beneficial for the development of activity recognition systems with non-wearable devices as the demand for such systems has increased recently.