  In this work we present a method to adaptively compensate for scale factor
errors in both rotational velocity and seeker angle measurements. The
adaptation scheme estimates the scale factor errors using a predictive coding
model implemented as a deep neural network with recurrent layer, and then uses
these estimates to compensate for the error. During training, the model learns
over a wide range of scale factor errors that ideally bound the expected errors
that can occur during deployment, allowing the deployed model to quickly adapt
in real time to the ground truth error. We demonstrate in a realistic six
degrees-of-freedom simulation of an exoatmospheric intercept that our method
effectively compensates for concurrent rotational velocity and seeker angle
scale factor errors. The compensation method is general in that it is
independent of a given guidance, navigation, and control system implementation.
Although demonstrated using an exoatmospheric missile with strapdown seeker,
the method is also applicable to endoatmospheric missiles with both gimbaled
and strapdown seekers, as well as general purpose inertial measurement unit
rate gyro compensation.
