Uncertainty-Based Non-Parametric Active Peak Detection
This addresses source localization for applications like sensor networks or signal processing, offering an incremental improvement in efficiency for low-sample scenarios.
The paper tackles the problem of active source localization by designing an uncertainty-based sampling algorithm to localize peaks from few energy measurements, showing that the error scales as O(log^2 m/m) and achieving superior performance in low-sample regimes compared to state-of-the-art passive and greedy methods.
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.