Problem
Research questions and friction points this paper is trying to address.
sensor selection
target localization
top-p hypothesis
set-valued decision
sequential hypothesis testing
Top-P Sensor Selection for Target Localization
🤖 AI Summary
This work addresses the limitations of conventional sensor selection methods that focus exclusively on identifying a single optimal hypothesis (top-1), which is insufficient for target localization tasks requiring multiple high-probability candidate nodes. To overcome this, the authors propose a set-based decision rule grounded in top-p hypothesis coverage, integrating geometric awareness within a sequential hypothesis testing framework to design a novel sensor selection algorithm. By replacing the traditional top-1 criterion with a top-p performance metric, the approach better aligns with practical demands for generating reliable candidate sets. Experimental results on a real-world platform demonstrate that the proposed method significantly improves coverage performance of the selected sensor node lists, outperforming existing top-1 strategies.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
top-p selection
set-valued decision
sensor selection
sequential hypothesis testing
geometry-aware algorithm
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