Abstract
Accurate signal localization is critical for Internet of Things applications,
but precise propagation models are often unavailable due to uncontrollable
factors. Simplified models such as planar and spherical wavefront
approximations are widely used but can cause model mismatches that reduce
accuracy. To address this, we propose an expected likelihood ratio framework
for model mismatch analysis and online model selection without requiring
knowledge of the true propagation model. The framework leverages the scenario
independent distribution of the likelihood ratio of the actual covariance
matrix, enabling the detection of mismatches and outliers by comparing given
models to a predefined distribution. When an accurate electromagnetic model is
unavailable, the robustness of the framework is analyzed using data generated
from a precise electromagnetic model and simplified models within positioning
algorithms. Validation in direct localization and reconfigurable intelligent
surface assisted scenarios demonstrates the ability to improve localization
accuracy and reliably detect model mismatches in diverse Internet of Things
environments.