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Geometrical Cluster-Based Scatterer Detection Method with the Movement of Mobile Terminal

Luan, Fengyu; Molisch, Andreas; Xiao, Limin; Tufvesson, Fredrik LU and Zhou, Shidong (2015) IEEE Vehicular Technology Conference (VTC Spring), 2015 In IEEE 81st Vehicular Technology Conference (VTC Spring), 2015
Abstract
When a mobile station moves along a trajectory, it will see different parts of the same scatterers or scatterer groups during its movement. In this paper, we present a new method to identify such physical clusters of scatterers, and the corresponding groups of multipath components (MPCs) interacting with those clusters, from measurements of channel impulse responses. The method is based on identifying MPCs that have similar long-term properties - in the sense that they ``effectively interact with'' the same physical clusters, The method consists of four steps: (1) estimate the delays, the DOAs and the amplitudes of the MPCs in each time snapshot; (2) track the MPCs in the time-delay domain; (3) localize all the scattering points in a 2-D... (More)
When a mobile station moves along a trajectory, it will see different parts of the same scatterers or scatterer groups during its movement. In this paper, we present a new method to identify such physical clusters of scatterers, and the corresponding groups of multipath components (MPCs) interacting with those clusters, from measurements of channel impulse responses. The method is based on identifying MPCs that have similar long-term properties - in the sense that they ``effectively interact with'' the same physical clusters, The method consists of four steps: (1) estimate the delays, the DOAs and the amplitudes of the MPCs in each time snapshot; (2) track the MPCs in the time-delay domain; (3) localize all the scattering points in a 2-D Cartesian coordinate system and cluster them on a map with a traditional Kmeans clustering algorithm; (4) merge the scattering points of different MPCs into physical clusters. The method is evaluated using both synthetic data and real measurement data from a suburban area. In the latter case, the locations and the sizes of the interacting objects identified from the measurements show excellent agreement with the location of physical objects in the environment such as pillars and buildings. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
MPC tracking, clustering, localization, physical clusters
in
IEEE 81st Vehicular Technology Conference (VTC Spring), 2015
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE Vehicular Technology Conference (VTC Spring), 2015
external identifiers
  • Scopus:84940416725
DOI
10.1109/VTCSpring.2015.7145852
language
English
LU publication?
yes
id
223ffdf0-8563-4818-80ff-81bb805ca416 (old id 7766991)
date added to LUP
2015-08-25 12:24:19
date last changed
2016-10-13 04:47:06
@misc{223ffdf0-8563-4818-80ff-81bb805ca416,
  abstract     = {When a mobile station moves along a trajectory, it will see different parts of the same scatterers or scatterer groups during its movement. In this paper, we present a new method to identify such physical clusters of scatterers, and the corresponding groups of multipath components (MPCs) interacting with those clusters, from measurements of channel impulse responses. The method is based on identifying MPCs that have similar long-term properties - in the sense that they ``effectively interact with'' the same physical clusters, The method consists of four steps: (1) estimate the delays, the DOAs and the amplitudes of the MPCs in each time snapshot; (2) track the MPCs in the time-delay domain; (3) localize all the scattering points in a 2-D Cartesian coordinate system and cluster them on a map with a traditional Kmeans clustering algorithm; (4) merge the scattering points of different MPCs into physical clusters. The method is evaluated using both synthetic data and real measurement data from a suburban area. In the latter case, the locations and the sizes of the interacting objects identified from the measurements show excellent agreement with the location of physical objects in the environment such as pillars and buildings.},
  author       = {Luan, Fengyu and Molisch, Andreas and Xiao, Limin and Tufvesson, Fredrik and Zhou, Shidong},
  keyword      = {MPC tracking,clustering,localization,physical clusters},
  language     = {eng},
  publisher    = {ARRAY(0xc4d6e10)},
  series       = {IEEE 81st Vehicular Technology Conference (VTC Spring), 2015},
  title        = {Geometrical Cluster-Based Scatterer Detection Method with the Movement of Mobile Terminal},
  url          = {http://dx.doi.org/10.1109/VTCSpring.2015.7145852},
  year         = {2015},
}