Second Order Cone Programming for Sensor Network Localization with Anchor Position Uncertainty

Node localization is a difficult task in sensor networks in which the ranging measurements are subject to errors and anchor positions are subject to uncertainty. In this paper, the robust localization problem is formulated using the maximum likelihood criterion under an unbounded uncertainty model f...

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Vydáno v:	IEEE transactions on wireless communications Ročník 13; číslo 2; s. 749 - 763
Hlavní autoři:	Naddafzadeh-Shirazi, Ghasem, Shenouda, Michael Botros, Lampe, Lutz
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.02.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Algorithms Anchors Applied sciences Complexity Complexity theory Detection, estimation, filtering, equalization, prediction Distance measurement distributed localization Exact sciences and technology Information, signal and communications theory Localization Mathematical models Networks Optimization Position (location) Programming robust optimization Robustness second order cone programming (SOCP) Services and terminals of telecommunications Signal and communications theory Signal, noise Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Telemetry. Remote supervision. Telewarning. Remote control Uncertainty wireless sensor networks Second order Range finding wireless sensor networks Wireless telecommunication robust optimization Iterative method Computational complexity Implementation Optimization Remote sensing Relaxation Accuracy Infinite medium Simulation Wireless network second order cone programming (SOCP) Convexity Maximum likelihood Localization Sensor array EM algorithm distributed localization Signal detection
ISSN:	1536-1276, 1558-2248
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Shrnutí:	Node localization is a difficult task in sensor networks in which the ranging measurements are subject to errors and anchor positions are subject to uncertainty. In this paper, the robust localization problem is formulated using the maximum likelihood criterion under an unbounded uncertainty model for the anchor positions. To overcome the non-convexity of the resulting optimization problem, a convex relaxation leading to second order cone programming (SOCP) is devised. Furthermore, an analysis is performed in order to identify the set of nodes which are accurately positioned using robust SOCP, and to establish a relation between the solution of the proposed robust SOCP optimization and the existing robust optimization using semidefinite programming (SDP). Based on this analysis, a mixed robust SDP-SOCP localization framework is proposed which benefits from the better accuracy of SDP and the lower complexity of SOCP. Since the centralized optimization involves a high computational complexity in large networks, we also derive the distributed implementation of the proposed robust SOCP convex relaxation. Finally, we propose an iterative optimization based on the expectation maximization (EM) algorithm for the cases where anchor uncertainty parameters are unavailable. Simulations confirm that the robust SOCP and mixed robust SDP-SOCP provide tradeoffs between localization accuracy and computational complexity that render them attractive solutions, especially in networks with a large number of nodes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1536-1276 1558-2248
DOI:	10.1109/TWC.2013.120613.130170