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宽带室内信道中微多普勒效应的建模、分析与仿真及通过摆实验进行验证

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

作者信息

Abdelgawwad Ahmed, Borhani Alireza, Pätzold Matthias

机构信息

Faculty of Engineering and Science, University of Agder, P.O. Box 509, 4898 Grimstad, Norway.

出版信息

Sensors (Basel). 2020 Feb 14;20(4):1049. doi: 10.3390/s20041049.

DOI:10.3390/s20041049
PMID:32075188
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7070821/
Abstract

This paper is about designing a 3D no n-stationary wideband indoor channel model for radio-frequency sensing. The proposed channel model allows for simulating the time-variant (TV) characteristics of the received signal of indoor channel in the presence of a moving object. The moving object is modelled by a point scatterer which travels along a trajectory. The trajectory is described by the object's TV speed, TV horizontal angle of motion, and TV vertical angle of motion. An expression of the TV Doppler frequency caused by the moving scatterer is derived. Furthermore, an expression of the TV complex channel transfer function (CTF) of the received signal is provided, which accounts for the influence of a moving object and fixed objects, such as walls, ceiling, and furniture. An approximate analytical solution of the spectrogram of the CTF is derived. The proposed channel model is confirmed by measurements obtained from a pendulum experiment. In the pendulum experiment, the trajectory of the pendulum has been measured by using an inertial-measurement unit (IMU) and simultaneously collecting CSI data. For validation, we have compared the spectrogram of the proposed channel model fed with IMU data with the spectrogram characteristics of the measured CSI data. The proposed channel model paves the way towards designing simulation-based activity recognition systems.

摘要

本文旨在设计一种用于射频传感的三维非平稳宽带室内信道模型。所提出的信道模型能够模拟在存在移动物体的情况下室内信道接收信号的时变特性。移动物体由沿轨迹移动的点散射体建模。该轨迹由物体的时变速度、时变水平运动角度和时变垂直运动角度描述。推导了由移动散射体引起的时变多普勒频率的表达式。此外,还给出了接收信号的时变复信道传递函数(CTF)的表达式,该表达式考虑了移动物体和固定物体(如墙壁、天花板和家具)的影响。推导了CTF频谱图的近似解析解。通过摆实验获得的测量结果验证了所提出的信道模型。在摆实验中,使用惯性测量单元(IMU)测量了摆的轨迹,并同时收集了CSI数据。为了进行验证,我们将输入IMU数据的所提出信道模型的频谱图与测量的CSI数据的频谱图特征进行了比较。所提出的信道模型为基于仿真的活动识别系统的设计铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/5355548ec79c/sensors-20-01049-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/b32678742b41/sensors-20-01049-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/c510386977f6/sensors-20-01049-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/83c71e976e92/sensors-20-01049-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/2111e6454dc6/sensors-20-01049-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/66c42ad12064/sensors-20-01049-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/94ab40adcc0e/sensors-20-01049-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/5355548ec79c/sensors-20-01049-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/b32678742b41/sensors-20-01049-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/c510386977f6/sensors-20-01049-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/83c71e976e92/sensors-20-01049-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/2111e6454dc6/sensors-20-01049-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/66c42ad12064/sensors-20-01049-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/94ab40adcc0e/sensors-20-01049-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ccf/7070821/5355548ec79c/sensors-20-01049-g007.jpg

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引用本文的文献

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本文引用的文献

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Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures.基于雷达微多普勒特征的非侵入式居家步态分析。
IEEE Trans Biomed Eng. 2019 Sep;66(9):2629-2640. doi: 10.1109/TBME.2019.2893528. Epub 2019 Jan 16.
2
Mitigation of CSI Temporal Phase Rotation with B2B Calibration Method for Fine-Grained Motion Detection Analysis on Commodity Wi-Fi Devices.采用 B2B 校准方法缓解 CSI 时间相位旋转,实现商品 Wi-Fi 设备上的细粒度运动检测分析。
Sensors (Basel). 2018 Nov 6;18(11):3795. doi: 10.3390/s18113795.
3
WiFi-Based Real-Time Calibration-Free Passive Human Motion Detection.
基于WiFi的实时免校准被动人体运动检测
Sensors (Basel). 2015 Dec 21;15(12):32213-29. doi: 10.3390/s151229896.