Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements

Chi-Sing Leung; H.C. So; Frankie K.W. Chan; A.G. Constantinides

doi:10.1007/978-3-642-34481-7_29

Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements

Chi-Sing Leung^*
, H.C. So
, Frankie K.W. Chan
, A.G. Constantinides

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Source localization can be achieved by making use of the time-of-arrival (TOA) measurements, but it is not a trivial task because the TOAs have nonlinear relationships with the source coordinates. This paper exploits a neural network technique, namely, Lagrange programming neural networks, for TOA-based localization. We also investigate the local stability of our formulation. Simulation results demonstrate that the performance of the proposed location estimator approaches the optimality benchmark of Cramér-Rao lower bound. © 2012 Springer-Verlag.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	19th international conference, ICONIP 2012, Proceedings
Editors	Tingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of Publication	Heidelberg
Publisher	Springer
Pages	234-241
Volume	II
ISBN (Electronic)	9783642344817, 364234481X
ISBN (Print)	9783642344800
DOIs	https://doi.org/10.1007/978-3-642-34481-7_29
Publication status	Published - Nov 2012
Event	19th International Conference on Neural Information Processing (ICONIP 2012) - Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012

Publication series

Name	Lecture Notes in Computer Science
Volume	7664
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Neural Information Processing (ICONIP 2012)
Place	Qatar
City	Doha
Period	12/11/12 → 15/11/12

Research Keywords

Analog network
source localization
time-of-arrival

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10.1007/978-3-642-34481-7_29

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Leung, C.-S., So, H. C., Chan, F. K. W., & Constantinides, A. G. (2012). Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), Neural Information Processing: 19th international conference, ICONIP 2012, Proceedings (Vol. II, pp. 234-241). (Lecture Notes in Computer Science; Vol. 7664). Springer . https://doi.org/10.1007/978-3-642-34481-7_29

Leung, Chi-Sing ; So, H.C. ; Chan, Frankie K.W. et al. / Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements. Neural Information Processing: 19th international conference, ICONIP 2012, Proceedings. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Vol. II Heidelberg : Springer , 2012. pp. 234-241 (Lecture Notes in Computer Science).

@inproceedings{76548f666a48410fae39e5af7989d28d,

title = "Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements",

abstract = "Source localization can be achieved by making use of the time-of-arrival (TOA) measurements, but it is not a trivial task because the TOAs have nonlinear relationships with the source coordinates. This paper exploits a neural network technique, namely, Lagrange programming neural networks, for TOA-based localization. We also investigate the local stability of our formulation. Simulation results demonstrate that the performance of the proposed location estimator approaches the optimality benchmark of Cram{\'e}r-Rao lower bound. {\textcopyright} 2012 Springer-Verlag.",

keywords = "Analog network, source localization, time-of-arrival",

author = "Chi-Sing Leung and H.C. So and Chan, \{Frankie K.W.\} and A.G. Constantinides",

year = "2012",

month = nov,

doi = "10.1007/978-3-642-34481-7\_29",

language = "English",

isbn = "9783642344800",

volume = "II",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "234--241",

editor = "Tingwen Huang and Zhigang Zeng and Chuandong Li and Leung, \{Chi Sing\}",

booktitle = "Neural Information Processing",

note = "19th International Conference on Neural Information Processing (ICONIP 2012) ; Conference date: 12-11-2012 Through 15-11-2012",

}

Leung, C-S , So, HC, Chan, FKW & Constantinides, AG 2012, Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements. in T Huang, Z Zeng, C Li & CS Leung (eds), Neural Information Processing: 19th international conference, ICONIP 2012, Proceedings. vol. II, Lecture Notes in Computer Science, vol. 7664, Springer , Heidelberg, pp. 234-241, 19th International Conference on Neural Information Processing (ICONIP 2012), Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34481-7_29

Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements. / Leung, Chi-Sing ; So, H.C.; Chan, Frankie K.W. et al.
Neural Information Processing: 19th international conference, ICONIP 2012, Proceedings. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. II Heidelberg: Springer , 2012. p. 234-241 (Lecture Notes in Computer Science; Vol. 7664).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements

AU - Leung, Chi-Sing

AU - So, H.C.

AU - Chan, Frankie K.W.

AU - Constantinides, A.G.

PY - 2012/11

Y1 - 2012/11

N2 - Source localization can be achieved by making use of the time-of-arrival (TOA) measurements, but it is not a trivial task because the TOAs have nonlinear relationships with the source coordinates. This paper exploits a neural network technique, namely, Lagrange programming neural networks, for TOA-based localization. We also investigate the local stability of our formulation. Simulation results demonstrate that the performance of the proposed location estimator approaches the optimality benchmark of Cramér-Rao lower bound. © 2012 Springer-Verlag.

AB - Source localization can be achieved by making use of the time-of-arrival (TOA) measurements, but it is not a trivial task because the TOAs have nonlinear relationships with the source coordinates. This paper exploits a neural network technique, namely, Lagrange programming neural networks, for TOA-based localization. We also investigate the local stability of our formulation. Simulation results demonstrate that the performance of the proposed location estimator approaches the optimality benchmark of Cramér-Rao lower bound. © 2012 Springer-Verlag.

KW - Analog network

KW - source localization

KW - time-of-arrival

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84869036205&origin=recordpage

U2 - 10.1007/978-3-642-34481-7_29

DO - 10.1007/978-3-642-34481-7_29

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783642344800

VL - II

T3 - Lecture Notes in Computer Science

SP - 234

EP - 241

BT - Neural Information Processing

A2 - Huang, Tingwen

A2 - Zeng, Zhigang

A2 - Li, Chuandong

A2 - Leung, Chi Sing

PB - Springer

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T2 - 19th International Conference on Neural Information Processing (ICONIP 2012)

Y2 - 12 November 2012 through 15 November 2012

ER -

Leung CS , So HC, Chan FKW, Constantinides AG. Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements. In Huang T, Zeng Z, Li C, Leung CS, editors, Neural Information Processing: 19th international conference, ICONIP 2012, Proceedings. Vol. II. Heidelberg: Springer . 2012. p. 234-241. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-34481-7_29

Analog Neural Network Approach for Source Localization Using Time-of-Arrival Measurements

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