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.

翻译：我们研究集值决策规则，其中性能由包含前$p$个假设来定义，而非仅包含最佳或真实假设。该准则源于目标跟踪中的传感器选择问题，即利用廉价测量识别出一组可能最接近目标的传感器节点列表。我们分析了序贯假设检验下前$p$选择与前$1$选择的性能，提出了一种几何感知的传感器选择算法，并通过真实测试平台数据验证了该方法。