Top-P Sensor Selection for Target Localization

2026-04-08

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.

Technology Category

• Search and Optimization: Heuristic Search
• Reasoning under Uncertainty: Sequential Decision Making
• Constraint Satisfaction and Optimization: Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• sensor selection
• target localization
• top-p hypothesis
• set-valued decision
• sequential hypothesis testing

Innovation

• top-p selection
• set-valued decision
• sensor selection
• sequential hypothesis testing
• geometry-aware algorithm