Node localization based on distributed constrained optimization using Jacobi's method

Henrique Ferraz; Amr Alanwar; Mani Srivastava; João P. Hespanha

doi:10.1109/CDC.2017.8264154

Node localization based on distributed constrained optimization using Jacobi's method

Henrique Ferraz, Amr Alanwar, Mani Srivastava, João P. Hespanha

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

We consider the spatial localization of nodes in a network, based on measurements of their relative position with respect to their neighbors. These measurements include the nodes' relative positions in a global coordinate system, their distances, or their pseudo ranges. We show that the maximum likelihood estimator associated with these localization problems can be viewed as a constrained optimization problem with a specific structure and provide a distributed algorithm to solve it. Under appropriate assumptions, it is shown that the maximum likelihood estimates are locally asymptotically stable equilibrium points of the proposed algorithm. As a case study, we consider a range-based localization problem and present simulation results to evaluate the algorithm.

Original language	English
Title of host publication	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3380-3385
Number of pages	6
ISBN (Electronic)	9781509028733
DOIs	https://doi.org/10.1109/CDC.2017.8264154
State	Published - 28 Jun 2017
Externally published	Yes
Event	56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia Duration: 12 Dec 2017 → 15 Dec 2017

Publication series

Name	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Volume	2018-January

Conference

Conference	56th IEEE Annual Conference on Decision and Control, CDC 2017
Country/Territory	Australia
City	Melbourne
Period	12/12/17 → 15/12/17

Access to Document

10.1109/CDC.2017.8264154

Cite this

Ferraz, H., Alanwar, A., Srivastava, M., & Hespanha, J. P. (2017). Node localization based on distributed constrained optimization using Jacobi's method. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp. 3380-3385). (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2017.8264154

@inproceedings{d5e136afb3f7409890cf363c4bd3b2f0,

title = "Node localization based on distributed constrained optimization using Jacobi's method",

abstract = "We consider the spatial localization of nodes in a network, based on measurements of their relative position with respect to their neighbors. These measurements include the nodes' relative positions in a global coordinate system, their distances, or their pseudo ranges. We show that the maximum likelihood estimator associated with these localization problems can be viewed as a constrained optimization problem with a specific structure and provide a distributed algorithm to solve it. Under appropriate assumptions, it is shown that the maximum likelihood estimates are locally asymptotically stable equilibrium points of the proposed algorithm. As a case study, we consider a range-based localization problem and present simulation results to evaluate the algorithm.",

author = "Henrique Ferraz and Amr Alanwar and Mani Srivastava and Hespanha, {Jo{\~a}o P.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 56th IEEE Annual Conference on Decision and Control, CDC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",

year = "2017",

month = jun,

day = "28",

doi = "10.1109/CDC.2017.8264154",

language = "English",

series = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3380--3385",

booktitle = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",

}

Ferraz, H, Alanwar, A, Srivastava, M & Hespanha, JP 2017, Node localization based on distributed constrained optimization using Jacobi's method. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 3380-3385, 56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne, Australia, 12/12/17. https://doi.org/10.1109/CDC.2017.8264154

Node localization based on distributed constrained optimization using Jacobi's method. / Ferraz, Henrique; Alanwar, Amr; Srivastava, Mani et al.
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3380-3385 (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Node localization based on distributed constrained optimization using Jacobi's method

AU - Ferraz, Henrique

AU - Alanwar, Amr

AU - Srivastava, Mani

AU - Hespanha, João P.

PY - 2017/6/28

Y1 - 2017/6/28

N2 - We consider the spatial localization of nodes in a network, based on measurements of their relative position with respect to their neighbors. These measurements include the nodes' relative positions in a global coordinate system, their distances, or their pseudo ranges. We show that the maximum likelihood estimator associated with these localization problems can be viewed as a constrained optimization problem with a specific structure and provide a distributed algorithm to solve it. Under appropriate assumptions, it is shown that the maximum likelihood estimates are locally asymptotically stable equilibrium points of the proposed algorithm. As a case study, we consider a range-based localization problem and present simulation results to evaluate the algorithm.

AB - We consider the spatial localization of nodes in a network, based on measurements of their relative position with respect to their neighbors. These measurements include the nodes' relative positions in a global coordinate system, their distances, or their pseudo ranges. We show that the maximum likelihood estimator associated with these localization problems can be viewed as a constrained optimization problem with a specific structure and provide a distributed algorithm to solve it. Under appropriate assumptions, it is shown that the maximum likelihood estimates are locally asymptotically stable equilibrium points of the proposed algorithm. As a case study, we consider a range-based localization problem and present simulation results to evaluate the algorithm.

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DO - 10.1109/CDC.2017.8264154

M3 - Conference contribution

AN - SCOPUS:85046143873

T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

SP - 3380

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BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017

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ER -

Ferraz H, Alanwar A, Srivastava M, Hespanha JP. Node localization based on distributed constrained optimization using Jacobi's method. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3380-3385. (2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017). doi: 10.1109/CDC.2017.8264154

Node localization based on distributed constrained optimization using Jacobi's method

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