Bayesian Integrity Monitoring for Cellular Positioning -- A Simplified Case Study

TL;DR

This paper introduces Bayesian RAIM algorithms for 1D cellular positioning, improving position accuracy and integrity monitoring by efficiently computing posterior probabilities, with simulations showing superior performance over existing methods.

Contribution

The paper presents a simplified Bayesian RAIM framework for cellular positioning that enables exact posterior computation and enhances integrity monitoring in a 1D Gaussian setting.

Findings

Bayesian RAIM achieves tighter protection levels.

Significant performance improvement over baseline RAIM.

Effective in meeting target integrity risk.

Abstract

Bayesian receiver autonomous integrity monitoring (RAIM) algorithms are developed for the snapshot cellular positioning problem in a simplified one-dimensional (1D) linear Gaussian setting. Position estimation, multi-fault detection and exclusion, and protection level (PL) computation are enabled by the efficient and exact computation of the position posterior probabilities via message passing along a factor graph. Computer simulations demonstrate the significant performance improvement of the proposed Bayesian RAIM algorithms over a baseline advanced RAIM algorithm, as it obtains tighter PLs that meet the target integrity risk (TIR) requirements.