Detecting and localizing border crossings using RF links

Abstract

Detecting and localizing a person crossing a line segment, i.e., border, is valuable information in security and data analytic applications. To that end, we use the received signal strength (RSS) measured on RF links between nodes deployed linearly along a border as a border crossing detection and localization system. RSS measurements from any single RF link are noisy and prone to variations due to environmental changes (e.g. branches moving in wind). The redundant overlapping nature of the links between pairs of nodes in our proposed system provides an opportunity to mitigate these issues. We propose a hidden Markov model (HMM) which models the RSS on network links as a function of the neighboring nodes between which a person crosses. We demonstrate that the forward-backward solution to this HMM provides a robust and real time border crossing detection and localization system.