Forward-Backward Map Matching for Airborne Magnetic Anomaly Navigation
Joseph O'Leary, Tapio Simula, Tom Kimpson, Corey Davey, Wenchao Li, Andrew D. Greentree, Allison Kealy
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.
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