Accuracy Evaluation Method of Stable Platform Inertial Navigation System Based on Quantum Neural Network

Abstract

This paper aims to reduce the structural complexity and navigation errors of stable platform inertial navigation system (INS). For this purpose, a new navigation error model was proposed for the stable platform INS based on quantum neural network (QNN). After reviewing several representative QNNs, the author established a QNN-based navigation error estimation algorithm on four coordinate systems, in which the angles of acceleration error were calibrated by twelve positions. Then, the QNN-based INS error model was constructed through training and testing. Next, the established model was applied to an experiment on two flight tracks with Kalman filter (KF), fixed-interval smoothing filter (smooth) and improved smoothing filter (improved). The results show that the model can significantly improve the multi-position calibration accuracy and the self-calibration accuracy of acceleration errors. The research findings shed new light on the accuracy evaluation of stable INS.