Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation
Abstract
Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric magnetic anomaly map. Existing map-matching algorithms either assume a Gaussian posterior (extended Kalman filter), require Monte Carlo sampling (particle filter), or discard posterior uncertainty in favour of a single optimal path (Viterbi algorithm). Here we formulate map matching as inference on a chain-structured factor graph and solve it segment by segment using the forward-backward algorithm, which yields a discrete posterior over candidate positions and its Shannon entropy Ht at time t. Over the \texttt{Renfrew\_395} map, 103 synthetic trials yield a median position error of $32.7$ m, 91% below the median inertial drift of $362$ m. On these trials and 103 flight paths, the median error is 43% below the extended Kalman filter and particle filter. Across these paths, a threshold on segment-level entropy detects 47% of segments with errors above two map cells and 94% of those above three, at a 10% false-alarm rate. The foregoing results indicate that Ht provides a truth-free diagnostic of map-conditioned navigation ambiguity.
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BibTeX
@misc{oleary2026forwardbackward,
title = {{Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation}},
author = {Joseph O'Leary and Tapio Simula and Tom Kimpson and Corey Davey and Wenchao Li and Andrew D. Greentree and Allison Kealy},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38047},
eprint = {2610.38047},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38047},
}
RIS
TY - UNPB
TI - Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation
AU - Joseph O'Leary
AU - Tapio Simula
AU - Tom Kimpson
AU - Corey Davey
AU - Wenchao Li
AU - Andrew D. Greentree
AU - Allison Kealy
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.38047
UR - https://arxiv.org/abs/2610.38047
AB - Airborne magnetic anomaly navigation provides a passive, unjammable alternative to Global Navigation Satellite Systems positioning by matching magnetometer measurements against a lithospheric magnetic anomaly map. Existing map-matching algorithms either assume a Gaussian posterior (extended Kalman filter), require Monte Carlo sampling (particle filter), or discard posterior uncertainty in favour of a single optimal path (Viterbi algorithm). Here we formulate map matching as inference on a chain-structured factor graph and solve it segment by segment using the forward-backward algorithm, which yields a discrete posterior over candidate positions and its Shannon entropy H_t at time t. Over the \texttt{Renfrew\_395} map, 10^3 synthetic trials yield a median position error of $32.7$ m, 91% below the median inertial drift of $362$ m. On these trials and 10^3 flight paths, the median error is 43% below the extended Kalman filter and particle filter. Across these paths, a threshold on segment-level entropy detects 47% of segments with errors above two map cells and 94% of those above three, at a 10% false-alarm rate. The foregoing results indicate that H_t provides a truth-free diagnostic of map-conditioned navigation ambiguity.
ER -