Top-P Sensor Selection for Target Localization
This work addresses sensor selection for target tracking, offering a more robust approach for identifying likely sensor nodes, though it appears incremental as it builds on existing sequential hypothesis testing frameworks.
The paper tackles the problem of sensor selection for target localization by introducing a top-p decision rule that includes multiple likely hypotheses, and validates the approach with real testbed data, showing improved performance over single-best selection methods.
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.