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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 13; no. 2; pp. 749 - 763
Main Authors: Naddafzadeh-Shirazi, Ghasem, Shenouda, Michael Botros, Lampe, Lutz
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.02.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography: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