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LocInFlow: A Flow-Based Information-Theoretic Model for Localization Systems
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DOI：10.1109/JISPIN.2026.3669521.png)
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Abstract
En 中文
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 Localization as Information Flow (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 blockwise comparison under explicit measurement assumptions, using standard deployment artifacts and routine calibration/evaluation data, without claiming universal predeployment error prediction. We illustrate LocInFlow by mapping a simplified Bluetooth low energy (BLE) angle-of-arrival system onto it, exposing dominant information-loss sources across functional blocks. Although introduced on a moderate BLE grid, LocInFlow scales to large infrastructures and 3-D scenarios, as its functional-block metrics are technology-independent and do not depend on scenario size or dimensionality.
Keywords:
Location awareness
Information theory
Measurement
Fingerprint recognition
Radio frequency
Stability analysis
Measurement uncertainty
Loss measurement
Estimation
Analytical models
Angle-of-arrival (AoA)
bluetooth low energy (BLE)
information theory
mutual information
Shannon-Weaver communication model
uncertainty quantification
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Papers:
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