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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.07020 |
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| _version_ | 1866915923907051520 |
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| author | Buyukkalayci, Kaan Pak, Kyle Karakas, Merve Li, Xinlin Fragouli, Christina |
| author_facet | Buyukkalayci, Kaan Pak, Kyle Karakas, Merve Li, Xinlin Fragouli, Christina |
| contents | We study set-valued decision rules in which performance is defined by the inclusion of the top-$p$ 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-$p$ versus top-$1$ selection under sequential hypothesis testing, propose a geometry-aware sensor selection algorithm, and validate the approach using real testbed data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07020 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Top-P Sensor Selection for Target Localization Buyukkalayci, Kaan Pak, Kyle Karakas, Merve Li, Xinlin Fragouli, Christina Information Theory We study set-valued decision rules in which performance is defined by the inclusion of the top-$p$ 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-$p$ versus top-$1$ selection under sequential hypothesis testing, propose a geometry-aware sensor selection algorithm, and validate the approach using real testbed data. |
| title | Top-P Sensor Selection for Target Localization |
| topic | Information Theory |
| url | https://arxiv.org/abs/2604.07020 |