Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems
Doctoral thesis
Published version
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https://hdl.handle.net/11250/2767221Utgivelsesdato
2021Metadata
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Originalversjon
Abdelgawwad, A. (2021). Synthetic Micro-Doppler Signatures of Non-Stationary Channels for the Design of Human Activity Recognition Systems (Doctoral thesis). University of Agder, Kristiansand.Sammendrag
The main aim of this dissertation is to generate synthetic micro-Doppler signatures and TV-MDSs to train the HACs. This is achieved by developing non-stationary fixed-tofixed (F2F) indoor channel models. Such models provide an in-depth understanding of the channel parameters that influence the micro-Doppler signatures and TV-MDSs. Hence, the proposed non-stationary channel models help to generate the micro-Doppler signatures and the TV-MDSs, which fit those of the collected measurement data.
First, we start with a simple two-dimensional (2D) non-stationary F2F channel model with fixed and moving scatterers. Such a model assumes that the moving scatterers are moving in 2D geometry with simple time variant (TV) trajectories and they have the same height as the transmitter and the receiver antennas. The model of the Doppler shifts caused by the moving scatterers in 2D space is provided. The micro-Doppler signature of this model is explored by employing the spectrogram of which a closed-form expression is derived. Moreover, we demonstrate how the TV-MDSs can be computed from the spectrograms.
The aforementioned model is extended to provide two three-dimensional (3D) nonstationary F2F channel models. Such models allow simulating the micro-Doppler signatures influenced by the 3D trajectories of human activities, such as walking and falling. Moreover, expressions of the trajectories of these human activities are also given. Approximate solutions of the spectrograms of these channels are provided by approximating the Doppler shifts caused by the human activities into linear piecewise functions of time. The impact of these activities on the micro-Doppler signatures and the TV-MDSs of the simulated channel models is explored.
The work done in this dissertation is not limited to analyzing micro-Doppler signatures and the TV-MDSs of the simulated channel models, but also includes those of the measured channels. The channel-state-information (CSI) software tool installed on commercial-off-theshelf (COTS) devices is utilized to capture complex channel transfer function (CTF) data under the influence of human activities. To mitigate the TV phase distortions caused by the clock asynchronization between the transmitter and receiver stations, a back-to-back (B2B) connection is employed. Models of the measured CTF and its true phases are also shown. The true micro-Doppler signatures and TV-MDSs of the measured CTF are analyzed. The results showed that the CSI tool is reliable to validate the proposed channel models. This allows the micro-Doppler signatures and the TV-MDSs extracted from the data collected with this tool to be used to train the HACs.
Består av
Paper I: Abdelgawwad, A. & Patzold, M. (2017). On the Influence of Walking People on the Doppler Spectral Characteristics of Indoor Channels. IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops. https://doi.org/10.1109/PIMRC.2017.8292482. Author´s accepted manuscript. Full-text is available in AURA as a separate file: http://hdl.handle.net/11250/2491623.Paper II: Abdelgawwad, A. & Patzold, M. (2018). A Framework for Activity Monitoring and Fall Detection Based on the Characteristics of Indoor Channels. IEEE Vehicular Technology Conference (VTC) Proceedings. https://doi.org/10.1109/VTCSpring.2018.8417468. Author´s accepted manuscript. Full-text is available in AURA as a separate file: http://hdl.handle.net/11250/2594883.
Paper III: Abdelgawwad, A. & Patzold, M. (2019). A 3D Non-Stationary Cluster Channel Model for Human Activity Recognition. IEEE Vehicular Technology Conference. https://doi.org/10.1109/VTCSpring.2019.8746345. Author´s accepted manuscript. Full-text is available in AURA as a separate file: https://hdl.handle.net/11250/2767189.
Paper IV: Abdelgawwad, A., Catala, A. & Patzold, M. (2020). Doppler Power Characteristics Obtained from Calibrated Channel State Information for Human Activity Recognition. IEEE Vehicular Technology Conference. https://doi.org/10.1109/VTC2020-Spring48590.2020.9129187. Author´s accepted manuscript. Full-text is available in AURA as a separate file: https://hdl.handle.net/11250/2735058.
Paper V: Abdelgawwad, A., Borhani, A. & Pätzold, M. U. (2020). Modelling, Analysis, and Simulation of the Micro-Doppler Effect in Wideband Indoor Channels with Confirmation Through Pendulum Experiments. Sensors, 20(4): 1049. https://doi.org/10.3390/s20041049. Published version. Full-text is available in AURA as a separate file: https://hdl.handle.net/11250/2727023.
Paper VI: Abdelgawwad, A., Catala, A. & Pätzold, M. U. (2020). A Trajectory-Driven 3D Channel Model for Human Activity Recognition. IEEE Access, 9, 103393 - 103406. https://doi.org/10.1109/ACCESS.2021.3098951. Published version. Full-text is available in AURA as a separate file: .