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dc.contributor.authorMa, Xiuxiu
dc.contributor.authorBallal, Tarig
dc.contributor.authorChen, Hui
dc.contributor.authorAldayel, Omar
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2021-02-03T08:01:31Z
dc.date.available2021-02-03T08:01:31Z
dc.date.issued2021
dc.identifier.citationMa, X., Ballal, T., Chen, H., Aldayel, O., & Alnaffouri, T. (2021). A Maximum-Likelihood TDOA Localization Algorithm Using Difference-of-Convex Programming. IEEE Signal Processing Letters, 1–1. doi:10.1109/lsp.2021.3051836
dc.identifier.issn1558-2361
dc.identifier.doi10.1109/LSP.2021.3051836
dc.identifier.urihttp://hdl.handle.net/10754/667203
dc.description.abstractA popular approach to estimate a source location using time difference of arrival (TDOA) measurements is to construct an objective function based on the maximum likelihood (ML) method. An iterative algorithm can be employed to minimize that objective function. The main challenge in this optimization process is the non-convexity of the objective function, which precludes the use of many standard convex optimization tools. Usually, approximations, such as convex relaxation, are applied, resulting in performance loss. In this work, we take advantage of difference-of-convex (DC) programming tools to develop an efficient solution to the ML TDOA localization problem. We show that, by using a simple trick, the objective function can be modified into an exact difference of two convex functions. Hence, tools from DC programming can be leveraged to carry out the optimization task, which guarantees convergence to a stationary point of the objective function. Simulation results show that, when initialized within the convex hull of the anchors, the proposed TDOA localization algorithm outperforms a number of benchmark methods, behaves as an exact ML estimator, and indeed achieves the Cramer-Rao lower bound.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9325001/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9325001
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectLocalization
dc.subjectTDOA
dc.subjectnon-convex
dc.subjectoptimization
dc.subjectCCCP
dc.subjectDC programming
dc.subjectdifference of convex
dc.subjectconvex-concave
dc.titleA Maximum-Likelihood TDOA Localization Algorithm Using Difference-of-Convex Programming
dc.typeArticle
dc.contributor.departmentCEMSE, King Abdullah University of Science and Technology, 127355 Thuwal, Saudi Arabia,
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalIEEE Signal Processing Letters
dc.eprint.versionPost-print
dc.contributor.institutionElectrical Engineering, King Saud University, 37850 Riyadh, Riyadh Province, Saudi Arabia,
dc.identifier.pages1-1
kaust.personMa, Xiuxiu
kaust.personBallal, Tarig
kaust.personChen, Hui
kaust.personAl-Naffouri, Tareq Y.
dc.identifier.eid2-s2.0-85099726600


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