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Paper IDE.4.5
Paper Title Minimax Lower Bounds for Circular Source Localization
Authors Aolin Xu, *, United States; Todd Coleman, UCSD, United States
Session E.4: Estimation and Applications
Presentation Lecture
Track Detection and Estimation
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract We consider the problem of estimating the unknown location of a spatial signal defined on a circular interval from noisy measurements. Lower bounds are derived for the minimax risk of the localization error defined by the circular distance. The lower bounds reveal the fundamental dependence of the localization error on various problem parameters, including the shape of the signal, the noise level, the length of the interval, the number of sensors, and the number of measurement trials. Le Cam's method and Fano's method are used for the derivation. All lower bounds are non-asymptotic, and different lower bounds are tight in different problem parameters. We also derive a Bayesian Cramer-Rao lower bound for the problem of linear source localization, which helps us understand the tightness of the lower bounds for the circular source in some asymptotic situations as well.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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