Top-P Sensor Selection for Target Localization
Information Theory
2026-04-09 v1 math.IT
Abstract
We study set-valued decision rules in which performance is defined by the inclusion of the top- hypotheses, rather than only the single best or true hypothesis. This criterion is motivated by sensor selection for target tracking, where inexpensive measurements are used to identify a list of sensor nodes that are likely to be closest to a target. We analyze the performance of top- versus top- selection under sequential hypothesis testing, propose a geometry-aware sensor selection algorithm, and validate the approach using real testbed data.
Cite
@article{arxiv.2604.07020,
title = {Top-P Sensor Selection for Target Localization},
author = {Kaan Buyukkalayci and Kyle Pak and Merve Karakas and Xinlin Li and Christina Fragouli},
journal= {arXiv preprint arXiv:2604.07020},
year = {2026}
}