Top-P Sensor Selection for Target Localization

TL;DR

This paper introduces a geometry-aware sensor selection algorithm for target localization that optimizes set-valued decision rules based on top-$p$ hypotheses, validated with real data.

Contribution

It proposes a novel sensor selection method for target tracking that considers multiple hypotheses and demonstrates its effectiveness through real-world validation.

Findings

The top-$p$ decision rule improves target localization accuracy.

The proposed algorithm outperforms traditional top-$1$ selection methods.

Validation on real testbed data confirms the approach's practical utility.

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.