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.

A non-stationary relay-based 3D MIMO channel model with time-variant path gains for human activity recognition in indoor environments

“…The contribution of the fixed scatterers S k ij is removed by applying a highpass filtering. For the remaining parameters as well as the scenario with constant path gains, we consider the same simulation parameters as described in [3,Section V].…”

“…To visualize the influence of introducing TV path gains to describe the motion of the person (modelled by a cluster of synchronized moving scatterers), we consider the spectrogram S ij (f , f, t) of the TVCTF H ij (f , t) using a Gaussian window [3,6].…”

“…According to [3], the spectrogram S ij (f , f, t) of the TV-CTF H ij (f , t) is given by the absolute square of the shorttime Fourier transform X ij (f , f, t) of the product of the TVCTF H ij (f , t) and a sliding Gaussian window h(t) of spread σ w . Therefore, the spectrogram S ij (f , f, t) of the TVCTF H ij (f , t) can be expressed as…”

“…The majority of the aforementioned studies are based on scenario specific multipath channel models, where the moving person or object is modelled by a single point scatterer, and assume constant path gains when expressing the received RF signal. A fixed-to-fixed (F2F) single-input single-output (SISO) three-dimensional (3D) channel model with constant path gains has been reported in [3], where the body segments of the moving person have been modelled by a cluster of synchronized moving point scatterers. The authors analyzed the effects of the motion of the person on the spectrogram of the complex channel gain, the TV mean Doppler shift (MDS), and TV Doppler spread (DS).…”

Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments

“…Then, this model has been extended for 2D no n-stationary fixed-to-fixed (F2F) indoor channels by considering the time-variant (TV) speed of the moving scatterer, angle of motion, angle of arrival, and angle of departure [6]. Later on, the TV Doppler frequency caused by the moving scatterer has been incorporated in three-dimensional (3D) channels by taking into account the TV azimuth angles of motion (AAOM), elevation angle of motion (EAOM), azimuth angle of departure (AAOD), elevation angle of departure (EAOD), azimuth angle of arrival (AAOA), and elevation angle of arrival (EAOA) for fixed-to-fixed channel models [6,7] and vehicle-to-vehicle channels [8]. To reveal the TV Doppler power characteristics of non-stationary multicomponent signals, a time-frequency distribution such as the spectrogram can be employed.…”