A Direct Localization Method of Multiple Distributed Sources Based on the Idea of Multiple Signal Classification

Yanqing REN; Zhiyu LU; Daming WANG; Jian LIU

doi:10.1587/transcom.2017EBP3199

A Direct Localization Method of Multiple Distributed Sources Based on the Idea of Multiple Signal Classification

Yanqing REN, Zhiyu LU, Daming WANG, Jian LIU

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The Localization of distributed sources has attracted significant interest recently. There mainly are two types of localization methods which are able to estimate distributed source positions: two-step methods and direct localization methods. Unfortunately, both fail to exploit the location information and so suffer a loss in localization accuracy. By utilizing the information not used in the above, a direct localization method of multiple distributed sources is proposed in this paper that offers improved location accuracy. We construct a direct localization model of multiple distributed sources and develop a direct localization estimator with the theory of multiple signal classification. The distributed source positions are estimated via a three-dimensional grid search. We also provide Cramer-Rao Bound, computational complexity analysis and Monte Carlo simulations. The simulations demonstrate that the proposed method outperforms the localization methods above in terms of accuracy and resolution.

Publication: IEICE TRANSACTIONS on Communications Vol.E101-B No.5 pp.1246-1256

Publication Date: 2018/05/01

Publicized: 2017/11/16

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2017EBP3199

Type of Manuscript: PAPER

Category: Antennas and Propagation

Authors

Yanqing REN
  Zhengzhou Institute of Information Science and Technology
Zhiyu LU
  Zhengzhou Institute of Information Science and Technology
Daming WANG
  Zhengzhou Institute of Information Science and Technology
Jian LIU
  Nanjing University of Posts and Telecommunications

Keyword

emitter localization, distributed sources, multiple signal classification, Cramer-Rao bound, array signal processing

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Yanqing REN, Zhiyu LU, Daming WANG, Jian LIU, "A Direct Localization Method of Multiple Distributed Sources Based on the Idea of Multiple Signal Classification" in IEICE TRANSACTIONS on Communications, vol. E101-B, no. 5, pp. 1246-1256, May 2018, doi: 10.1587/transcom.2017EBP3199.
Abstract: The Localization of distributed sources has attracted significant interest recently. There mainly are two types of localization methods which are able to estimate distributed source positions: two-step methods and direct localization methods. Unfortunately, both fail to exploit the location information and so suffer a loss in localization accuracy. By utilizing the information not used in the above, a direct localization method of multiple distributed sources is proposed in this paper that offers improved location accuracy. We construct a direct localization model of multiple distributed sources and develop a direct localization estimator with the theory of multiple signal classification. The distributed source positions are estimated via a three-dimensional grid search. We also provide Cramer-Rao Bound, computational complexity analysis and Monte Carlo simulations. The simulations demonstrate that the proposed method outperforms the localization methods above in terms of accuracy and resolution.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3199/_p

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@ARTICLE{e101-b_5_1246,
author={Yanqing REN, Zhiyu LU, Daming WANG, Jian LIU, },
journal={IEICE TRANSACTIONS on Communications},
title={A Direct Localization Method of Multiple Distributed Sources Based on the Idea of Multiple Signal Classification},
year={2018},
volume={E101-B},
number={5},
pages={1246-1256},
abstract={The Localization of distributed sources has attracted significant interest recently. There mainly are two types of localization methods which are able to estimate distributed source positions: two-step methods and direct localization methods. Unfortunately, both fail to exploit the location information and so suffer a loss in localization accuracy. By utilizing the information not used in the above, a direct localization method of multiple distributed sources is proposed in this paper that offers improved location accuracy. We construct a direct localization model of multiple distributed sources and develop a direct localization estimator with the theory of multiple signal classification. The distributed source positions are estimated via a three-dimensional grid search. We also provide Cramer-Rao Bound, computational complexity analysis and Monte Carlo simulations. The simulations demonstrate that the proposed method outperforms the localization methods above in terms of accuracy and resolution.},
keywords={},
doi={10.1587/transcom.2017EBP3199},
ISSN={1745-1345},
month={May},}

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TY - JOUR
TI - A Direct Localization Method of Multiple Distributed Sources Based on the Idea of Multiple Signal Classification
T2 - IEICE TRANSACTIONS on Communications
SP - 1246
EP - 1256
AU - Yanqing REN
AU - Zhiyu LU
AU - Daming WANG
AU - Jian LIU
PY - 2018
DO - 10.1587/transcom.2017EBP3199
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2018
AB - The Localization of distributed sources has attracted significant interest recently. There mainly are two types of localization methods which are able to estimate distributed source positions: two-step methods and direct localization methods. Unfortunately, both fail to exploit the location information and so suffer a loss in localization accuracy. By utilizing the information not used in the above, a direct localization method of multiple distributed sources is proposed in this paper that offers improved location accuracy. We construct a direct localization model of multiple distributed sources and develop a direct localization estimator with the theory of multiple signal classification. The distributed source positions are estimated via a three-dimensional grid search. We also provide Cramer-Rao Bound, computational complexity analysis and Monte Carlo simulations. The simulations demonstrate that the proposed method outperforms the localization methods above in terms of accuracy and resolution.
ER -