Theory of semidefinite programming for sensor network localization

• 成果类型: Article; Proceedings Paper
• 署名作者: So, Anthony Man-Cho; Ye, Yinyu
• 署名单位: Stanford University; Stanford University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-006-0040-1
• 发表日期: 2007
• 页码: 367-384
• 关键词: distance matrix completion; geometry; RIGIDITY

摘要

We analyze the semidefinite programming (SDP) based model and method for the position estimation problem in sensor network localization and other Euclidean distance geometry applications. We use SDP duality and interior-point algorithm theories to prove that the SDP localizes any network or graph that has unique sensor positions to fit given distance measures. Therefore, we show, for the first time, that these networks can be localized in polynomial time. We also give a simple and efficient criterion for checking whether a given instance of the localization problem has a unique realization in R-2 using graph rigidity theory. Finally, we introduce a notion called strong localizability and show that the SDP model will identify all strongly localizable sub-networks in the input network.