Uncertainty Estimation in the Neural Model for Aeromagnetic Compensation

Measuring the performance of an aeromagnetic compensation system is usually difficult. The standard deviation of the signal has been used as an index in the industry. While the standard deviation is drawn from frequency statistics, it cannot represent the performance on a single sampling point. On the other hand, as the true geomagnetic intensity is unknown, the signal's deviation is actually an approximate measurement of the residual error. This letter first analyzes the traditional neural model for aeromagnetic compensation to reveal the fact that the model can only estimate the expectation of interference. Then, we introduce a stochastic hidden variable to predict the standard deviation synchronously. The proposed model is derived from variational inference and trained as a stochastic gradient variational Bayes estimator. Simulations are performed to show the correlation between the true residual error and the estimated standard deviation.