A Priori Prediction of Geophysical Anomaly Navigation Fixability via Posterior Cramér-Rao Bounds and Machine Learning

Geophysical map matching works, but not everywhere. This work predicts where it will work: relating vehicle trajectory characteristics and geophysical map properties to achievable navigation accuracy, using the Fisher information matrix and the posterior Cramér-Rao bound as theoretical limits and a learned regression model to generalize from them. The result behaves like a sensor specification — given this trajectory over this map, position is bounded to within a stated precision — which makes geophysical aiding something that can be budgeted for in advance rather than tried and measured after the fact.

Supporting code: geo-fixability.

Recommended citation: Brodovsky, J. "A Priori Prediction of Geophysical Anomaly Navigation Fixability via Posterior Cramér-Rao Bounds and Machine Learning." IEEE Transactions on Aerospace and Electronic Systems (under review).