- Title
- Efficient deterministic algorithm for huge-sized noisy sensor localization problems via canonical duality theory
- Creator
- Latorre, Vittorio; Gao, David
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180615
- Identifier
- vital:15783
- Identifier
-
https://doi.org/10.1109/TCYB.2019.2891112
- Identifier
- ISBN:2168-2267 (ISSN)
- Abstract
- This paper presents a new deterministic method and a polynomial-time algorithm for solving general huge-sized sensor network localization problems. The problem is first formulated as a nonconvex minimization, which was considered as an NP-hard based on conventional theories. However, by the canonical duality theory, this challenging problem can be equivalently converted into a convex dual problem. By introducing a new optimality measure, a powerful canonical primal-dual interior (CPDI) point algorithm is developed which can solve efficiently huge-sized problems with hundreds of thousands of sensors. The new method is compared with the popular methods in the literature. Results show that the CPDI algorithm is not only faster than the benchmarks but also much more accurate on networks affected by noise on the distances. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Transactions on Cybernetics Vol. 51, no. 10 (2021), p. 5069-5081
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2019 IEEE
- Subject
- 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0906 Electrical and Electronic Engineering; Canonical duality theory; Deterministic algorithm; Global optimization; Sensor network localization (SNL) problem
- Reviewed
- Funder
- This work was supported by the U.S. Air Force Office of Scientific Research under Grant FA9550-17-1-0151.
- Hits: 670
- Visitors: 612
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|