Direct tuning of inertia sensors of a navigation system using the neural network approach

Abstract

In the present article, the inverse problem concerning the identification of suitable sensitivities for inertia measurement sensors, part of an inertial navigation system, is discussed. For a precise tuning of the navigation system, it is assumed that the sensitivities could modify their effective values as the consequence of instantaneous movements and environmental conditions. The goal is to set up a tool that is able to foresee any possible sensitivity scattering. The key point is to find a strategy to teach a neural network (NN) to be able to correct measurements when auxiliary global positioning systems (GPS) are not properly working. In this article the strategy is presented and discussed and several tests show some examples in which the procedure was successful or not. However, for all unsuccessful cases, it is possible to recognize the malfunctioning prior to complete the identification; therefore, data to discard in the NN training are easily identified. The final NN application is not discussed within this article because it is non-essential for the present aim that is the definition of optimal NN output parameters.