Robust Primal-Dual Proximal Algorithm for Cooperative Localization in WSNs

Mei Zhang, Xiaojing Shen, Zhiguo Wang, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

This paper addresses the localization challenge in cooperative multi-agent wireless sensor networks, specifically focusing on range-based localization. To enhance robustness against outliers in range measurements, we employ the Huber function, leading to the formulation of a robust yet nonconvex optimization problem with coupled agent variables. Confronted with this nonconvex optimization challenge, particularly in largescale networks, we reformulate the problem using Lagrange duality and conjugate theory. This restructuring yields subproblems characterized by smooth strong convexity for dual variables and a simplified form for primal variables, thereby facilitating an efficient solution. Building upon this reformulation, we introduce a novel distributed primal-dual algorithm that employs coordinate descent and proximal minimization techniques within an iterative framework. This approach furnishes closed-form solutions for both primal and dual variables. Theoretically, our method ensures not only the convergence of the sequence of objective function values but also, by leveraging the KurdykaŁojasiewicz property, we establish the guaranteed global convergence of the location estimates sequence to a critical point of the original objective function. Notably, our proposed approach exhibits lower computational complexity, communication cost, and storage space compared to existing methods. Numerical experiments underscore the superiority of the proposed method in terms of robustness and localization accuracy when compared to the other methods in the literature.

Original languageEnglish (US)
Title of host publicationFUSION 2024 - 27th International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781737749769
DOIs
StatePublished - 2024
Event27th International Conference on Information Fusion, FUSION 2024 - Venice, Italy
Duration: Jul 7 2024Jul 11 2024

Publication series

NameFUSION 2024 - 27th International Conference on Information Fusion

Conference

Conference27th International Conference on Information Fusion, FUSION 2024
Country/TerritoryItaly
CityVenice
Period7/7/247/11/24

Keywords

  • Huber loss
  • non-convex optimization
  • Primal-dual algorithm
  • wireless sensor network localization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing
  • Information Systems and Management

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