TY - GEN
T1 - Robust Primal-Dual Proximal Algorithm for Cooperative Localization in WSNs
AU - Zhang, Mei
AU - Shen, Xiaojing
AU - Wang, Zhiguo
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2024 ISIF.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Huber loss
KW - non-convex optimization
KW - Primal-dual algorithm
KW - wireless sensor network localization
UR - http://www.scopus.com/inward/record.url?scp=85207692171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85207692171&partnerID=8YFLogxK
U2 - 10.23919/FUSION59988.2024.10706474
DO - 10.23919/FUSION59988.2024.10706474
M3 - Conference contribution
AN - SCOPUS:85207692171
T3 - FUSION 2024 - 27th International Conference on Information Fusion
BT - FUSION 2024 - 27th International Conference on Information Fusion
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Information Fusion, FUSION 2024
Y2 - 7 July 2024 through 11 July 2024
ER -