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
- 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7766991
- author
- Luan, Fengyu
; Molisch, Andreas
; Xiao, Limin
; Tufvesson, Fredrik
LU
and Zhou, Shidong
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- MPC tracking, clustering, localization, physical clusters
- host publication
- 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
- conference location
- Glasgow, United Kingdom
- conference dates
- 2015-05-11 - 2015-05-14
- 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
- 2016-04-04 11:40:39
- date last changed
- 2022-02-06 07:41:59
@inproceedings{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}},
booktitle = {{IEEE 81st Vehicular Technology Conference (VTC Spring), 2015}},
keywords = {{MPC tracking; clustering; localization; physical clusters}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Geometrical Cluster-Based Scatterer Detection Method with the Movement of Mobile Terminal}},
url = {{http://dx.doi.org/10.1109/VTCSpring.2015.7145852}},
doi = {{10.1109/VTCSpring.2015.7145852}},
year = {{2015}},
}