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

Glen Hjelmerud Mørkbak Sørensen; Torleiv H. Bryne; Kristoffer Gryte; Tor Arne Johansen

eess.SYFeb 19, 2026

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

Authors: Glen Hjelmerud Mørkbak Sørensen, Torleiv H. Bryne, Kristoffer Gryte, Tor Arne Johansen

Organizations: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU) NO-7491 Trondheim, Norway

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.

Figures & tables

Figure 1: Multirotor drone and ground station in the field. The Bluetooth antenna array (green) is mounted flat.

Figure 2: NED position computed from measurements.

Panel of Figure 3 — Figure 3: Estimator performance without mitigation of BLE PARS outliers. Note the shaded sections roughly segmenting the data based on RTK/PARS and the manoeuvre performed during the given time interval.

		N [m]	E [m]	D [m]	$\phi$ [ ∘ ]	$\theta$ [ ∘ ]	$\psi$ [ ∘ ]
RTK	NT	0.399	0.486	0.499	1.175	1.206	23.185
	TK	0.458	0.578	0.575	1.165	1.186	23.168
	GM	0.458	0.578	0.575	1.165	1.186	23.168
SEG. 1	NT	0.566	0.389	0.267	0.866	3.231	41.935
	TK	0.567	0.395	0.248	0.855	3.197	41.907
	GM	0.552	0.398	0.247	0.855	3.197	41.907

Table 1: RMSE BLE angular + RTK range.

		N [m]	E [m]	D [m]	$\phi$ [ ∘ ]	$\theta$ [ ∘ ]	$\psi$ [ ∘ ]
RTK	NT	0.399	0.487	0.392	1.170	1.207	23.185
	TK	0.455	0.578	0.378	1.161	1.185	23.168
	GM	0.455	0.578	0.378	1.161	1.185	23.168
SEG. 1	NT	0.657	0.401	0.332	0.869	3.233	41.938
	TK	0.632	0.404	0.337	0.857	3.195	41.907
	GM	0.616	0.407	0.336	0.857	3.195	41.907

Table 2: RMSE BLE angular + barometer.

Panel of Figure 4 — Figure 4: Estimation error and $3\sigma$ -bounds in handover from RTK to BLE angular and RTK range.

Panel of Figure 5 — Figure 5: Estimation error and $3\sigma$ -bounds in handover from RTK to BLE angular and barometric pressure.

GNSS-Denied Localization

Global Navigation Satellite System (GNSS)-denied localization estimates position when satellite signals are unavailable or unreliable.

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In Global Navigation Satellite System (GNSS)-degraded environments, pseudolites (PLs) provide additional signal sources to enhance positioning performance, but their integration in optimization-based frameworks remains limited. This paper presents a loosely coupled factor graph optimization (FGO) framework that fuses the GNSS/PL least-squares (LS) solutions with inertial measurement unit (IMU) data. The evaluation considers low GNSS visibility scenarios with four high-elevation GNSS satellites and up to two PL transmitters over an 80~s window. FGO achieves a 22.8% to 41.3% reduction in mean 3D error compared to standard LS methods. Compared to a GNSS-IMU baseline, incorporating PL transmitters further improves positioning accuracy, with performance depending on geometry.

Abstract

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