LocInFlow: A Flow-Based Information-Theoretic Model for Localization Systems

Radio-based localization systems determine target positions by analyzing radio communication between sensor nodes. Despite numerous technological approaches, no generic information-theoretic modeling exists to compare such systems, even though a unified view of position information flow forms the conceptual link between all RF-based localization systems. This often yields suboptimal designs, where processing mismatches and hardware limits cause information loss and reduced localization performance. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LocInFlow</i>, the first model that maps RF-based localization systems onto the functional blocks of the Shannon–Weaver communication model, enabling technology-independent analysis of position information flow. LocInFlow introduces three metrics: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-posteriori uncertainty matrix</i> for geometric coding, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Modulation Mutual Information</i> for modulation, and the system-level <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flow efficiency</i>. The metrics do not target perfect reconstruction; they expose where information is lost and enable block- wise comparison under explicit measurement assumptions, using standard deployment artifacts and routine calibration/evaluation data, without claiming universal pre-deployment error prediction. We illustrate LocInFlow by mapping a simplified BLE AoA system onto it, exposing dominant information-loss sources across functional blocks. Although introduced on a moderate BLE grid, LocInFlow scales to large infrastructures and 3D scenarios, as its functional-block metrics are technology-independent and do not depend on scenario size or dimensionality.