Active, non-parametric peak detection is considered. As a use case, active source localization is examined and an uncertainty-based sampling scheme algorithm to effectively localize the peak from a few energy measurements is designed. It is shown that under very mild conditions, the source localization error with $m$ actively chosen energy measurements scales as $O(\log^2 m/m)$. Numerically, it is shown that in low-sample regimes, the proposed method enjoys superior performance on several types of data and outperforms the state-of-the-art passive source localization approaches and in the low sample regime, can outperform greedy methods as well.

翻译：作为使用案例,对主动源本地化进行了研究,并设计了基于不确定性的采样方法算法,以便从一些能源测量中有效地将峰值本地化,结果显示,在非常温和的条件下,源本地化误差,以积极选择的能量测量尺度为$O(\log ⁇ 2 m/m)美元(美元/m)为美元(美元/美元),从数字上看,在低抽样制度中,拟议方法在几种数据类型上表现优异,优于最先进的被动源本地化方法和低抽样制度中,也优于贪婪方法。