Improving 3D Cellular Positioning Integrity with Bayesian RAIM

TL;DR

This paper presents a Bayesian RAIM algorithm for 3D cellular positioning that improves integrity and protection levels by accurately modeling measurement uncertainties with Gaussian mixtures, outperforming traditional methods.

Contribution

It introduces a Bayesian RAIM approach that computes the exact posterior PDF as a Gaussian mixture, enhancing positioning integrity without discarding faulty measurements.

Findings

Achieves over 50% reduction in protection levels compared to baseline

Retains all measurement information, improving accuracy and integrity

Maintains computational efficiency similar to existing algorithms

Abstract

Ensuring positioning integrity amid faulty measurements is crucial for safety-critical applications, making receiver autonomous integrity monitoring (RAIM) indispensable. This paper introduces a Bayesian RAIM algorithm with a streamlined architecture for snapshot-type 3D cellular positioning. Unlike traditional frequentist-type RAIM algorithms, it computes the exact posterior probability density function (PDF) of the position vector as a Gaussian mixture (GM) model using efficient message passing along a factor graph. This Bayesian approach retains all crucial information from the measurements, eliminates the need to discard faulty measurements, and results in tighter protection levels (PLs) in 3D space and 1D/2D subspaces that meet target integrity risk (TIR) requirements. Numerical simulations demonstrate that the Bayesian RAIM algorithm significantly outperforms a baseline algorithm, achieving over $50\%$ PL reduction at a comparable computational cost.