Bluetooth Phased-array Aided Inertial Navigation Using Factor Graphs: Experimental Verification

2026-02-19

AI Summary

This work proposes a tightly coupled multi-sensor fusion approach based on factor graph optimization to address the limited accuracy of low-cost navigation systems in GNSS-denied environments, such as warehouse logistics and drone landing scenarios. For the first time, high-noise angle measurements from a commercial Bluetooth phased array are integrated into real-world multirotor flight experiments, combined with ranging or barometric altitude data and inertial measurements to construct a robust positioning system. By incorporating robust estimation techniques, the method effectively mitigates performance degradation caused by short-range Bluetooth signals and measurement noise. Experimental results demonstrate that the proposed system maintains high-precision localization even under GNSS outages, confirming its practical feasibility and robustness in real-world applications.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such as warehouse logistics, drone landings, and autonomous docking. Basing a navigation system off of commercial-off-the-shelf components may reduce the barrier of entry for phased-array radio navigation systems, albeit at the cost of significantly noisier measurements and relatively short feasible range. In this paper, we compare robust estimation strategies for a factor graph optimisation-based estimator using experimental data collected from multirotor drone flight. We evaluate performance in loss-of-GNSS scenarios when aided by Bluetooth angular measurements, as well as range or barometric pressure.

Problem

• Bluetooth phased-array
• inertial navigation
• GNSS-denied
• factor graphs
• robust estimation

Innovation

• Bluetooth phased-array
• factor graph optimization
• inertial navigation
• GNSS-denied navigation
• robust estimation