Polarised-Light-Aided Magnetometer Calibration

Developed an online calibration filter fusing celestial polarisation cues with magnetic field updates to resolve yaw-calibration ambiguities.

Problem

Online magnetometer calibration is critical for GNSS-denied navigation. However, heading estimation and magnetic calibration states are coupled: errors in yaw can be incorrectly absorbed as hard-iron biases or scale-factor distortions. This coupling often causes recursive filters to diverge or settle into self-consistent but incorrect states under large initial attitude errors.

Approach

• Celestial Aiding: Fused polarisation-camera bearing cues (generated using physically consistent sky models via PySkyLumos) to provide an independent celestial heading vector.
• State Partitioning: Implemented a Rao-Blackwellised Particle Filter (RBPF) where non-Gaussian attitude hypotheses are sampled via particles, while the remaining 32-dimensional conditional states (including calibration parameters) are recursively updated using per-particle Unscented Kalman Filters (UKFs).
• Adaptive Admission Gate: Formulated an online consistency check based on circular heading dispersion and innovation residuals. Magnetometer calibration updates are deferred during large attitude transients and only admitted once the celestial cue has concentrated the heading.

Key Highlights

• Guaranteed Convergence: Achieved a 99.99% final-heading success rate with a median yaw error of 0.26° in extensive Monte Carlo sweeps under initial heading offsets of up to 180°.
• Decoupled Ambiguity: Effectively resolved the yaw-calibration coupling, allowing reliable online estimation of hard-iron and scale-factor parameters.
• Resilient to Outages: Validated against canopy-dropout route scenarios and high-altitude UAV flight profiles, maintaining heading stability and calibration accuracy during extended optical blockages.