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 LocInFlow, 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 a-posteriori uncertainty matrix for geometric coding, the Modulation Mutual Information for modulation, and the system-level flow efficiency. 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.