Back to Search
Start Over
Tackling the flip ambiguity in wireless sensor network localization and beyond
- Source :
- Digital Signal Processing. 55:85-97
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- There have been significant advances in range-based numerical methods for sensor network localizations over the past decade. However, there remain a few challenges to be resolved to satisfaction. Those issues include, for example, the flip ambiguity, high level of noises in distance measurements, and irregular topology of the concerning network. Each or a combination of them often severely degrades the otherwise good performance of existing methods. Integrating the connectivity constraints is an effective way to deal with those issues. However, there are too many of such constraints, especially in a large and sparse network. This presents a challenging computational problem to existing methods. In this paper, we propose a convex optimization model based on the Euclidean Distance Matrix (EDM). In our model, the connectivity constraints can be simply represented as lower and upper bounds on the elements of EDM, resulting in a standard 3-block quadratic conic programming, which can be efficiently solved by a recently proposed 3-block alternating direction method of multipliers. Numerical experiments show that the EDM model effectively eliminates the flip ambiguity and retains robustness in terms of being resistance to irregular wireless sensor network topology and high noise levels.
- Subjects :
- Mathematical optimization
media_common.quotation_subject
010103 numerical & computational mathematics
02 engineering and technology
Euclidean distance matrix
01 natural sciences
Quadratic equation
Artificial Intelligence
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
0101 mathematics
Electrical and Electronic Engineering
media_common
Mathematics
Applied Mathematics
Numerical analysis
020206 networking & telecommunications
Ambiguity
Computational Theory and Mathematics
Signal Processing
Convex optimization
Computer Vision and Pattern Recognition
Statistics, Probability and Uncertainty
Computational problem
Algorithm
Wireless sensor network
Subjects
Details
- ISSN :
- 10512004
- Volume :
- 55
- Database :
- OpenAIRE
- Journal :
- Digital Signal Processing
- Accession number :
- edsair.doi.dedup.....0da76dfbde0995749a4b9689b8886e55