Near-Field Localization with Physics-Compliant Electromagnetic Model: Algorithms and Model Mismatch Analysis

TL;DR

This paper introduces a likelihood ratio framework for detecting model mismatches in electromagnetic signal localization, enhancing accuracy and robustness in IoT applications without needing the true propagation model.

Contribution

It proposes a novel framework for online model mismatch detection and selection that does not require knowledge of the true electromagnetic propagation model.

Findings

Framework effectively detects model mismatches and outliers.

Improves localization accuracy in IoT scenarios.

Validates robustness using electromagnetic and simplified models.

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.