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Distributed localization using Levenberg-Marquardt algorithm
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-9520-5153
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
C3 Ai, CA USA.
2021 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2021, no 1, article id 74Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a distributed algorithm for sensor network localization based on a maximum likelihood formulation. It relies on the Levenberg-Marquardt algorithm where the computations are distributed among different computational agents using message passing, or equivalently dynamic programming. The resulting algorithm provides a good localization accuracy, and it converges to the same solution as its centralized counterpart. Moreover, it requires fewer iterations and communications between computational agents as compared to first-order methods. The performance of the algorithm is demonstrated with extensive simulations in Julia in which it is shown that our method outperforms distributed methods that are based on approximate maximum likelihood formulations.

Place, publisher, year, edition, pages
SPRINGER , 2021. Vol. 2021, no 1, article id 74
Keywords [en]
Distributed localization; Maximum likelihood estimation; Message passing; Dynamic programming; Levenberg-Marquardt; Nonlinear least-squares
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-178732DOI: 10.1186/s13634-021-00768-wISI: 000687679400001OAI: oai:DiVA.org:liu-178732DiVA, id: diva2:1588879
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; Linkoping University

Available from: 2021-08-30 Created: 2021-08-30 Last updated: 2023-04-03
In thesis
1. Distributed Optimization for Control and Estimation
Open this publication in new window or tab >>Distributed Optimization for Control and Estimation
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Adopting centralized optimization approaches in order to solve optimization problem arising from analyzing large-scale systems, requires a powerful computational unit. Such units, however, do not always exist. In addition, it is not always possible to form the optimization problem in a centralized manner due to structural constraints or privacy requirements. A possible solution in these cases is to use distributed optimization approaches. Many large-scale systems have inherent structures which can be exploited to develop scalable optimization approaches. In this thesis, chordal graph properties are used in order to design tailored distributed optimization approaches for applications in control and estimation, and especially for model predictive control and localization problems. The first contribution concerns a distributed primal-dual interior-point algorithm for which it is investigated how parallelism can be exploited. In particular, it is shown how the computations of the algorithm can be distributed on different processors so that they can be run in parallel. As a result, the algorithm execution time is accelerated compared to the case where the algorithm is run on a single processor. Simulation studies on linear model predictive control and robust model predictive control confirm the efficiency of the framework. The second contribution is to devise a tailored distributed algorithm for nonlinear least squares with application to a sensor network location problem. It relies on the Levenberg-Marquardt algorithm, in which the computations are distributed using message passing over the computational graph of the problem, which is obtained from what is known as the clique tree of the problem. The results indicate that the algorithm provides not only a good localization accuracy, but also it requires fewer iterations and communications between computational agents in order to converge compared to known first-order methods. The third contribution is a study of extending the message passing idea in order to design tailored distributed algorithm for general non-convex problems. The framework relies on an augmented Lagrangian algorithm in which a primal-dual interior-point method is used for the inner iteration. Application of the framework for general model predictive control of systems with several interconnected sub-systems is extensively investigated. The performance of the framework is then compared with distributed methods based on the alternating direction method of multipliers, where the superiority of the framework is illustrated.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 26
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2207
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-182567 (URN)10.3384/9789179291983 (DOI)9789179291976 (ISBN)9789179291983 (ISBN)
Public defence
2022-03-11, Ada Lovelace, B-building, Campus Valla, Linköping, 10:00 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

ISBN has been added for the PDF version.

Funded by the Knut and Alice Wallenberg Foundation

Available from: 2022-02-04 Created: 2022-01-27 Last updated: 2023-04-03Bibliographically approved

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